U.S. patent application number 15/602012 was filed with the patent office on 2017-09-07 for method and apparatus for simultaneous localization and mapping of mobile robot environment.
This patent application is currently assigned to NEATO ROBOTICS, INC. The applicant listed for this patent is NEATO ROBOTICS, INC.. Invention is credited to Steven ALEXANDER, Mark EMMERICH, Vladimir ERMAKOV, Nathaniel David MONSON, Boris SOFMAN.
Application Number | 20170255203 15/602012 |
Document ID | / |
Family ID | 42941963 |
Filed Date | 2017-09-07 |
United States Patent
Application |
20170255203 |
Kind Code |
A1 |
SOFMAN; Boris ; et
al. |
September 7, 2017 |
METHOD AND APPARATUS FOR SIMULTANEOUS LOCALIZATION AND MAPPING OF
MOBILE ROBOT ENVIRONMENT
Abstract
Techniques that optimize performance of simultaneous
localization and mapping (SLAM) processes for mobile devices,
typically a mobile robot. In one embodiment, erroneous particles
are introduced to the particle filtering process of localization.
Monitoring the weights of the erroneous particles relative to the
particles maintained for SLAM provides a verification that the
robot is localized and detection that it is no longer localized. In
another embodiment, cell-based grid mapping of a mobile robot's
environment also monitors cells for changes in their probability of
occupancy. Cells with a changing occupancy probability are marked
as dynamic and updating of such cells to the map is suspended or
modified until their individual occupancy probabilities have
stabilized. In another embodiment, mapping is suspended when it is
determined that the device is acquiring data regarding its physical
environment in such a way that use of the data for mapping will
incorporate distortions into the map, as for example when the
robotic device is tilted.
Inventors: |
SOFMAN; Boris; (Pittsburgh,
PA) ; ERMAKOV; Vladimir; (Santa Clara, CA) ;
EMMERICH; Mark; (San Jose, CA) ; ALEXANDER;
Steven; (Fremont, CA) ; MONSON; Nathaniel David;
(Mountain View, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
NEATO ROBOTICS, INC. |
Newark |
CA |
US |
|
|
Assignee: |
NEATO ROBOTICS, INC,
Newark
CA
|
Family ID: |
42941963 |
Appl. No.: |
15/602012 |
Filed: |
May 22, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14543508 |
Nov 17, 2014 |
9678509 |
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15602012 |
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14067705 |
Oct 30, 2013 |
8903589 |
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14543508 |
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12873018 |
Aug 31, 2010 |
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14067705 |
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61238597 |
Aug 31, 2009 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G05D 2201/0203 20130101;
G05D 1/024 20130101; Y10S 901/47 20130101; B25J 11/0085 20130101;
Y10S 901/01 20130101; B25J 9/1602 20130101; B25J 9/0003 20130101;
G05D 1/0274 20130101 |
International
Class: |
G05D 1/02 20060101
G05D001/02; B25J 11/00 20060101 B25J011/00; B25J 9/16 20060101
B25J009/16; B25J 9/00 20060101 B25J009/00 |
Claims
1. A method for controlling movement of a mobile robot, the method
comprising: generating map data identifying the mobile robot's
physical environment; creating a map of the mobile robot's physical
environment and recording the location of the mobile robot within
the mobile robot's physical environment in response to the map
data; determining whether the mobile robot has become delocalized;
and suspending the generating of the map data while the mobile
robot is delocalized.
2. The method of claim 1 wherein the determining whether the mobile
robot has become delocalized comprises distinguishing between a
delocalization event and the detection of a moving object.
3. The method of claim 1 wherein the delocalization event is a
tilting of the mobile robot.
4. The method of claim 1 wherein the determination of whether the
mobile robot has become delocalized comprises determination of
whether the mobile robot's position in the physical environment is
erroneous.
5. The method of claim 1 further comprising resuming the generating
of map data when the mobile robot has become re-localized, with a
determination that the mobile robot has become re-localized
including hysteresis that provides for a period of time during
which the generating of map data is stable.
6. The method of claim 1 wherein generating map data comprises
providing mobile robot position updates.
7. The method of claim 1 wherein when the distance to an object is
larger than the distance to a boundary on the map, a delocalization
event is indicated.
8. The method of claim 1 wherein determining whether the mobile
robot has become delocalized comprises using the output of an
accelerometer.
9. A method for controlling movement of a mobile robot, the method
comprising: obtaining position data of objects relative to the
mobile robot; obtaining pose data of the mobile robot; generating,
from the position data and the pose data, using a SLAM algorithm,
map data identifying the mobile robot's physical environment;
creating a map of the mobile robot's physical environment and
recording the location of the mobile robot within the mobile
robot's physical environment in response to the map data;
determining whether the mobile robot has become delocalized;
suspending the generating of the map data while the mobile robot is
delocalized; and resuming the generating of map data when the
mobile robot has become re-localized, with a determination that the
mobile robot has become re-localized including hysteresis that
provides for a period of time during which the generating of map
data is stable.
10. The method of claim 9 wherein the position data is obtained
using a laser rangefinder.
11. The method of claim 9 wherein the determining whether the
mobile robot has become delocalized comprises distinguishing
between a delocalization event and the detection of a moving
object.
12. The method of claim 9 wherein the determination of whether the
mobile robot has become delocalized comprises determination of
whether the mobile robot's position in the physical environment is
erroneous.
13. A mobile device tracking system for a mobile device in its
physical environment comprising: a spatial sensor mounted on the
mobile device and configured to scan the physical environment; a
map generator configured to generate and update a map from the
spatial data, the map including the current position of the mobile
device; and a delocalization detector configured to generate a
current estimate by estimating the current position within said map
by generating position particles indicating at least one of
position and orientation of the mobile device within its physical
environment, the delocalization detector being further configured
to generate and iteratively maintain a data set of said position
particles to track a changing position of the mobile device within
its physical environment, the delocalization detector including: i.
a particle weight assignor that assigns a weight to each position
particle, the particle weight being a relative measure of the
likelihood that the position particle accurately represents the
current position with respect to other particles; ii. an erroneous
particle generator that introduces erroneous particles having
weights that are uniformly low with respect to the weights of the
position particles; and iii. a particle weight comparator that
compares the weights of the erroneous particles and the weights of
the position particles and determines that the mobile device has
become delocalized when a substantial number of erroneous particles
have weights that are no longer uniformly low with respect to the
weights of the position particles.
14. The system of claim 13 wherein the erroneous particles comprise
erroneous position particles.
15. The system of claim 13 wherein the determination of whether the
mobile device has become delocalized comprises determination of
whether the mobile device's position in the physical environment is
erroneous.
16. The system of claim 14 wherein the erroneous position particles
comprise erroneous inclination particles.
17. The system of claim 13 wherein the determination of whether the
mobile device has become delocalized comprises determination of
whether the mobile device's inclination in the physical environment
is erroneous.
18. The system of claim 17 wherein the determination of whether the
mobile device's inclination in the physical environment is
erroneous comprises determination of whether a tilt event has
occurred.
19. The system of claim 13 wherein the particle weight comparator
is further configured to calculate and monitor the averaged weight
or the median weight of the erroneous particles over multiple
iterations.
20. The mobile device tracking system of claim 13, wherein the
erroneous particle generator selects erroneous particles so as to
avoid introduction of additional error into the current estimate of
the current position.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] The present application is a continuation of U.S. patent
application Ser. No. 14,543,508, filed Nov. 17, 2014, which is a
divisional application of U.S. application Ser. No. 14/067,705,
filed Oct. 30, 2013, issued as U.S. Pat. No. 8,903,589 on Dec. 2,
2014, which is a divisional application of U.S. application Ser.
No. 12/873,018, filed Aug. 31, 2010 (abandoned) which claims the
benefit of co-pending U.S. provisional application Ser. No.
61/238,597, filed Aug. 31, 2009, entitled "Computation Optimization
Techniques for Simultaneous Localization and Mapping". The
disclosures of these applications are incorporated by reference
herein in their entirety.
BACKGROUND OF THE INVENTION
[0002] Aspects of the present invention relate to mobile robots,
and more particularly to the mapping of environments in which
mobile robots operate, to facilitate movement of mobile robots
within those environments.
[0003] As a system that enables a mobile robot to map its
environment and maintain working data of its position within that
map, simultaneous localization and mapping (SLAM) is both accurate
and versatile. Its reliability and suitability for a variety of
applications make it a useful element for imparting a robot with
some level of autonomy.
[0004] Typically, however, SLAM techniques tend to be
computationally intensive and thus their efficient execution often
requires a level of processing power and memory capacity that may
not be cost effective for some consumer product applications.
[0005] For those facing the low-cost production targets necessary
for competition in the consumer market, it is unlikely that an
economic hardware environment would include processing and memory
capacities capable of supporting adequately a robust SLAM system.
It therefore is imperative that developers seek ways to facilitate
efficient execution of the core SLAM algorithms within the limits
of the computational capacities they have. Generally, such
optimization schemes would seek to use processing power and system
bandwidth judiciously, which might mean simplifying some of the
SLAM algorithms in ways that do not critically compromise their
performance, or reducing input data size or bandwidth.
BRIEF SUMMARY OF THE INVENTION
[0006] Four concepts are outlined herein, each intended to enable a
SLAM system to maintain efficiency when it is operating on a
platform that provides limited processor power and/or memory
capacity. Some of these optimization methods may reside entirely in
software, or may require some element of hardware support to
function properly.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1 depicts a block diagram of aspects of a data
acquisition system in accordance with one embodiment of the present
invention, relating to system orientation.
[0008] FIG. 2 depicts a flow of operation of aspects of the FIG. 1
embodiment involving reaction to system orientation.
[0009] FIG. 3 depicts a block diagram of map generation and
delocalization detection apparatus in accordance with one
embodiment of the present invention.
[0010] FIG. 4 depicts a flow of operation of the FIG. 3 system
using particle weight comparison to detect delocalization.
[0011] FIG. 5 depicts an example of particle weight distribution
for a typical localization iteration process.
[0012] FIG. 6 depicts an example of particle weight distribution
for a localization iteration process when the robot is properly
localized.
[0013] FIG. 7 depicts an example of particle weight distribution
for a localization iteration process when the robot is
delocalized.
[0014] FIG. 8 depicts a plot of localized and delocalized states
based on verified particle distribution data.
[0015] FIG. 9 depicts a block diagram of a data acquisition system
and map/model processing apparatus in accordance with one
embodiment of the present invention.
[0016] FIG. 10 depicts a flow of operation of the system and
apparatus in FIG. 9 assigned probabilities of cell occupancy to
determine whether a cell is a dynamic cell.
[0017] FIG. 11 depicts an example of orientation of a mobile robot
in its physical environment.
[0018] FIG. 12 depicts an example of orientation of a mobile robot
in its physical environment when the robot is tilted.
[0019] FIG. 13 depicts a flow of operation of the system in FIG. 1
that uses threshold limits for map elements to determine map or
model generation.
[0020] FIG. 14 depicts one scenario of movement and orientation of
a mobile robot in its physical environment.
[0021] FIG. 15 depicts a scenario of movement and orientation of a
mobile robot in its physical environment in the presence of an
obstacle.
[0022] FIG. 16 depicts a scenario of movement and orientation of a
mobile robot in its physical environment when the robot is
tilted.
DETAILED DESCRIPTION OF THE INVENTION
[0023] 1. Suspending Robot Pose Updates during Delocalization
[0024] Localization requires regularly updating a robot's pose
(position and angle) within its environment. The frequency with
which this is done can affect overall system performance, depending
on how often data must be processed as a result of an update
operation. Minimizing computational load is essential to providing
a SLAM system that can function effectively in a low-cost hardware
environment.
[0025] According to one feature of the invention, computational
load may be reduced by eliminating robot position updates when it
appears that the robot has become delocalized, in which case the
updates likely would be erroneous anyway.
[0026] FIG. 1 is a diagram depicting aspects of the just-mentioned
feature in a mobile robotic system 100. In FIG. 1, data acquisition
system 110 generates data regarding the environment of mobile robot
120. This data becomes input data to processing apparatus 130. From
this data, processing apparatus 130 generates a map or model of the
mobile robot's environment (block 132). Processing apparatus 130
also may contain a separate function (block 134) that monitors the
generation or updating of the map for any shift in map elements
beyond a threshold limit. If such an occurrence is detected, the
processing apparatus (block 136) responds by executing instructions
to suspend or modify the use of data from data acquisition system
110. A sensing unit 140 also may monitor the data acquisition
system 110 for a loss in preferred orientation of the data
acquisition system 110 for data generation. If sensing unit 140
detects a loss in orientation, processing apparatus 130 will
respond by executing instructions to suspend or modify use of data
generated by the data acquisition system 110. Mobile robot 120 may
be connected to processing apparatus 130. The sensing unit 140, if
present, may be attached to the mobile robot 120. Data acquisition
system 110 may be attached to the mobile robot 120 as well, or
alternatively may be separate.
[0027] FIG. 2 shows a flow of operation of the system depicted in
FIG. 1. In FIG. 2, at block 201, the data acquisition system
generates data regarding the robot's physical environment, yielding
the generated data at block 202. At block 203, the orientation of
the data acquisition system is monitored to see whether the data
acquisition system is maintaining its preferred orientation with
respect to the robot's physical environment (e.g. whether the data
acquisition system is tilting, has tipped over, or otherwise seems
to display an orientation other than one in which the robot can
function within its physical environment. At block 204, if the
preferred orientation is not lost, then at block 205, the generated
data is used to generate or update the map of the robot's physical
environment. On the other hand, if at block 204, if the preferred
orientation is lost, then at block 206, the map generation is
suspended, or the map is modified. After either block 205 or block
206, flow returns to the top of FIG. 1 to generate data and monitor
the orientation of the data acquisition system.
[0028] FIG. 3 is a diagram showing other features of the invention.
In FIG. 3, map generation apparatus 310 provides a map of a mobile
device's environment for localization of the mobile device within
that environment. A delocalization detection apparatus 320 uses the
map information to determine the position of the device. Particle
generation apparatus 322 generates particles representing potential
poses of the mobile device. Particle weight assignment apparatus
324 assigns weights to each particle representing its relative
likelihood of accuracy relative to other particles. Separately, an
erroneous particle generation apparatus 326 generates particles
such that their corresponding weights as generated by particle
weight assignment apparatus 324 will be low, representing a low
probability of correctly indicating the mobile device's position. A
particle weight comparison apparatus 328 compares the weights of
the erroneous particles with the weights of the particles generated
by the particle generation apparatus 322 and confirms that the
device is accurately localized or determines whether delocalization
has occurred.
[0029] The method may operate as follows:
[0030] 1) Erroneous position and inclination particles may be
introduced to the set of tracking particles. The erroneous
particles, also referred to later as verification particles, may be
selected in a way that they likely will not introduce additional
error into the current estimate of the robot's position and
inclination.
[0031] 2) Typically, erroneous particles have low weights, which
may correspond generally to their low probability of accurately
representing the robot's current position. If the erroneous
particles have weights that are not uniformly low, but rather may
be a distribution or some combination of low and high weights, then
this may imply that the robot has become delocalized.
[0032] 3) If it is determined that the robot likely is delocalized,
then updating its position within the map of its surroundings may
be suspended until the weights of the erroneous particles return to
a more uniform distribution of low values.
[0033] FIG. 4 depicts a flow of operation of the system depicted in
FIG. 3. In FIG. 4, at block 401, the existing map may be used or
updated as appropriate. At block 402, particles are generated,
either anew or iteratively, the iteratively generated particles
being added to the existing particle set. At block 403, weights are
assigned to each particle. At block 404, erroneous particles are
generated, and at block 405, the erroneous particles have weights
assigned to them. At block 406, the weights of the erroneous
particles are compared to those of the original particle set to
determine whether delocalization has occurred. At block 407, a
check for delocalization is made. If delocalization has not
occurred, then similarly to block 205 in FIG. 2, map generation and
updating continues. If delocalization has occurred, then similarly
to block 206 in FIG. 2, map generation is suspended or
modified.
[0034] There are precautionary reasons why this procedure is
implemented in a SLAM system and it may afford other advantages
beyond computational load reduction. Suspension of mapping when
delocalization is detected may avoid corrupting the map. Also, once
delocalization is detected, additional actions can be enabled to
improve the likelihood that the robot will re-localize, such as
increasing the number of particles in the set or employing looser
error models. Depending on the severity of the delocalization,
other actions might be taken aside from those that are related to
recovery. For example, the robot might stop or restart its run.
EXAMPLE
Determining Delocalization through Introduction of Erroneous
Particles
[0035] A typical approach to localization under a SLAM scheme might
include the following steps:
[0036] 1) For each particle: [0037] a) Apply an ideal motion model
(e.g., odometry). [0038] b) Apply position and angle (x,y,.theta.)
adjustments drawn from error model distributions. [0039] c)
Evaluate with respect to the current map to compute weight.
[0040] 2) Resample particles proportional to computed weights.
[0041] A typical localization iteration based on the above process
might yield the particle weight distribution illustrated in FIG.
5.
[0042] In FIG. 5, the distribution of particles, sorted by weight,
appears as a curve, indicating a mix of particles of low, middle
and high weights. The particles with higher weights--those at the
upper left side of the distribution--have a proportionally higher
probability of representing accurately the robot's pose relative to
other particles lower on the sorted distribution of weights. When
the particles are indexed by their weights, a particle's index
number may indicate its relative position with respect to other
particles regarding its probability of accurately representing the
robot's pose (position and angle). Within such a framework,
particle 1 has the highest probability of accuracy and all
subsequent particles (i.e., particles 2, 3, 4, etc.) have
sequentially lower probabilities of accuracy in their pose.
[0043] It is worth noting that the weight scale (the vertical axis
in the graph) may be highly dependent on environmental conditions
such as distance from walls, number of valid distance readings from
a spatial sensor such as a laser rangefinder, etc. An approach to
determining delocalization via the introduction of erroneous
particles generally should be independent of environmental
conditions.
[0044] The goal of introducing erroneous particles is to identify
when the particles with higher probability of representing the
robot's pose are not much better than particles with the lowest
probability of representing the robot's pose. In such a
circumstance, the implication is that most or all potential poses
are bad, and therefore the robot has little or no reliable
information regarding its actual whereabouts within its
environment. By definition, the robot is delocalized.
[0045] The process of assessing the state of localization involves
introducing additional test particles whose pose is deliberately
erroneous in order to set a baseline weight for comparison to
better particles.
[0046] It is often observed that particle evaluation is most
sensitive to angular errors. Small changes in robot angle, for
example, can translate to large errors in distance measurements as
the distance from the robot to an object in its surrounding
environment increases. Large angular errors can have similar
distributions of laser readings in terms of distance, but they may
dramatically reduce the overall weight of the full particle
set.
[0047] Typically, the particles representing candidate location
angles with the highest weights are fairly close to an ideal motion
model. Recognizing this, a generally effective approach to
delocalization detection is to introduce erroneous particles at the
center of the ideal motion model with large offsets to the angle
(e.g., .+-.30.degree., 40.degree., 50.degree., 60.degree.,
etc.).
[0048] If the robot is properly localized, the erroneous particles
will reside relatively close together at the end of the sorted
distribution curve that contains the lowest weighted particles, as
shown in FIG. 6.
[0049] In FIG. 6, the erroneous particles, referred to here as
verification particles for their purpose, are clustered together on
the lower right end of the curve, each having a weight that is
closer to zero than the particles comprising the rest of the sorted
distribution.
[0050] If the robot is delocalized, many normal particles will have
low weights, and many of these are likely to have weights lower
than some of the erroneous or verification particles, as seen in
FIG. 7.
[0051] In FIG. 7, some erroneous (verification) particles reside at
the far right side of the distribution, but other erroneous
particles are scattered through the rest of the particle set. As
more particles known to be erroneous have weights that exceed
other, non-verification particles, it becomes increasingly likely
that the robot has delocalized.
Identifying Delocalization
[0052] The actual determination of delocalization can be done in
any of a variety of ways, including by examining the mean index
value of the erroneous (verification) particles. In a localized
condition, most or all of the erroneous particles will reside
relatively close together at the bottom of the index, since they
generally will have the lowest weights. Averaging the indices of
the erroneous particles in a localized case will yield a large
number relative to the size of the total set of particles,
including both erroneous and non-erroneous particles.
[0053] In a delocalized state, however, the erroneous particles are
scattered through the distribution curve, and an indexing of
particles in order of their weight will yield a set of erroneous
particles whose averaged index is not necessarily high with respect
to the size of the total set of particles. Generally, an average of
verification particle indices that remains constant and high in
value with respect to total particle set size reflects a localized
condition. An average that falls in value or begins to fluctuate in
value may indicate a delocalized condition.
[0054] Both of these states, localized and delocalized, are
depicted in the plots of the averaged verification particle data in
FIG. 8. In this graph, the plotted data are the averaged
verification particle indices. For localization iterations 1
through 600, the averaged data are high and relatively constant,
which is consistent with a localized state. Shortly after iteration
600, the average value drops significantly and then recovers; in
this particular data set, this drop corresponds to an engineer
picking the robot up from the floor and moving it to a different
location. Like the previous drops in index average, the return of
the average to a high, stable number indicates that the robot
likely recovered from the event.
[0055] At a point on the graph between 800 and 1000 localization
iterations the data begins to fluctuate greatly. The lack of
consistency in the average and the range of its variability are
indicative of a delocalized condition. Unlike the previous, large
delocalization, the robot likely was unable to recover from this
delocalization as indicated by the data's continuing instability
through the end of the data set.
[0056] Determining that the robot has delocalized relies on
comparing the averaged erroneous particle index to a threshold
number. The threshold number can be decided a priori during coding,
but it is typically beneficial to include some hysteresis in the
evaluation of whether a robot is localized. For example, looking at
the latter portion of the data set illustrated in FIG. 8, the
variability of the averaged verification particle indices reaches a
high number several times, but, in each instance, it drops again
after only a few iterations. A proper evaluation of whether a robot
has recovered from a delocalization event should not look only at
instantaneous values, but also should evaluate whether the averaged
index returns to a high value and remains stable at a high value
for a period of time sufficient to demonstrate that the robot
likely has successfully re-localized. The necessary minimum
duration can also be defined in the code.
2. Treatment of Dynamic Areas of the Map
[0057] One of the challenges confronting a robot engaged in
creation and update of maps of its surroundings is the potential
mix of static and dynamic elements within its surroundings. While
it is generally expected that most of a robot's surroundings will
remain fixed, a robot should be prepared to function within an
environment in which people, pets, etc. may be moving.
[0058] Newly encountered, unmapped space may contain a mix of
dynamic and static elements. Making a distinction between the
robot's identification of potentially dynamic areas of the map and
those that are static is essential for building useful and accurate
maps for the robot to use.
[0059] In an embodiment, the issue of distinguishing between static
(permanent) elements of the robot's surroundings and dynamic
(transient) elements may be addressed in the following way:
[0060] 1) The robot may create an abstraction of its environment (a
map) within a grid-space of cells available in memory, each cell
containing a number that indicates a relative probability of
whether the space within the cell is empty or occupied. These
values may range from, for example, zero (empty) to 254 (occupied),
with an initial condition value within every cell of 127 (i.e., a
value in the middle of the spectrum).
[0061] 2) A spatial sensor, most conveniently a laser rangefinder,
may scan the robot's surroundings, measuring distances to
boundaries and other objects. This data stream may provide the base
information from which the robot can determine the probability that
a cell is occupied or not. For example, if the spatial sensor
measures a distance to a wall, the occupancy probability that the
cell on the robot-generated map corresponding to that point along
the wall is occupied increases while the occupancy probability for
all the cells along the measurement vector between the robot and
the wall decreases (because the wall was the first object
detected). With repeated measurement from the spatial sensor, the
probabilities may become more certain.
[0062] 3) If a cell currently identified as empty has an occupancy
probability that is changing (e.g., appearing suddenly to be
occupied), it may signify a potentially dynamic area of the
map.
[0063] 4) If such cells are detected, they may be marked so as to
not be updated with regard to their likelihood of containing an
obstacle while they are dynamic. Similarly, this also can extend to
an arbitrary zone surrounding these cells.
[0064] FIG. 9 is a diagram of a system containing other features of
the invention. In FIG. 9, a data acquisition system 910 generates
data regarding the physical environment of a mobile device such as
a robot. The data generated by the data acquisition system provides
input to a map/model processing apparatus 920. The map/model
processing apparatus 920 generates and maintains a map in a
cell-based grid form (block 922) and assigns a probability of
occupancy to each cell (block 924) based on the data received from
the data acquisition system. Additionally, the map/model processing
unit monitors individual cells (block 926) for changes in their
probability of occupancy. Based on the detection of such changes,
the processing unit determines if any cells are dynamic. If cells
are determined to be dynamic, they are marked accordingly (block
928). Mapping or updating of such cells is suspended for the period
that they are in a dynamic state.
[0065] FIG. 10 depicts a flow of operation of the embodiment shown
in FIG. 9. In FIG. 10, at block 1001, the data acquisition system
generates data regarding the robot's physical environment, yielding
the generated data at block 1002. At block 1003, the generated data
is used to generate or update the map of the robot's physical
environment. At block 1004, probabilities of occupancy for each
cell in the grid map are assigned or updated. At block 1005, it is
determined whether probabilities of occupancy of any of the cells
are changing. If they are not, then flow returns to block 1001. If
they are, then at block 1006, the cells whose probabilities of
occupancy are changing are marked as dynamic so that they are not
updated while probability of occupancy is changing. Flow then
returns to block 1001.
Addressing Tilt in a Sensor Used to Collect Spatial Data Regarding
a Robot's Surroundings
[0066] Accurate delineation of a robot's surroundings as part of
mapping and localization requires maintaining the orientations of
the sensors generating spatial data in congruence with the
presiding surfaces of the surrounding geometry. For a robot
operating inside a building or similar enclosure, this means that a
sensor collecting information in two dimensions would preferably
maintain its plane of detection as parallel to the floor since the
floor would define the dominant plane of motion available to a
robot traversing it.
[0067] Because floors may have areas of uneven surface or surface
discontinuities, or because objects resting on the floor may
introduce non-uniformities in a robot's available travel surface,
it is possible that a sensor collecting spatial data may not
maintain consistent orientation with the presiding surfaces of the
surrounding geometry, which can lead to erroneous delineation of
the robot's surroundings.
[0068] FIGS. 11-12 illustrate the potential problem encountered by
a robot collecting spatial data without an ability to detect when
its sensor has lost parallel orientation with the floor. In the
upper illustration, the robot is traveling away from a physical
boundary at A and toward a physical boundary at B. A sensor mounted
on the robot in this example is collecting spatial data in a
horizontal plane indicated by the thin line positioned at a height
near the top of the robot. In the lower illustration, the robot
begins traversing an obstacle which tilts the robot backward. If
the robot does not recognize that it is no longer collecting data
in a plane that is accordant with the surrounding geometry, then
the spatial construction developed from the sensor data will not
match the actual geometry defined by the robot's surroundings. In
this case, the data collection plane's forward incline will distort
the previously determined position of the wall at B to one further
out, at B'. The backward decline on the data collection plane
results in its intersection with the floor, creating the impression
that a boundary exists behind the robot at A' rather than at the
further position of A.
[0069] Often, wheel slip accompanies tilt when a robot traverses a
substantive irregularity in a floor surface. This can be
particularly problematic if it occurs when the robot is collecting
its first data on a new area (e.g., when the robot has turned a
corner into an unmapped space) since the distorted image may be
incorporated into the map.
[0070] For a robot using the continuous generation of spatial
boundary information to provide updates to a map, erroneous data
generated during a tilt event can propagate into mapping or
localization algorithms. The potential results may include some
degree of mapping corruption, which frequently can lead to
delocalization.
[0071] Consequently, it is important to provide a strategy to
identify and address tilt conditions during normal operation, and
two approaches to same are described below. These approaches are
designed such that they can be used separately or together in
potential reinforcement.
3. Tilt as Detected and Addressed in Software
[0072] Typically, dynamic areas created by people, pets or objects
moved or in use by a person will present a dynamic area to mark,
one that usually is limited in its footprint. However, if the
dynamic area is spread along a relatively wide area, then this may
represent a different scenario. For example, if a map boundary area
shifts suddenly or moves in a way that many, possibly contiguous
cells are tagged as active, then it may be likely that the robot
has tilted. In such a case, the spatial sensor's detection plane
may be angled such that a portion of the floor near the robot is
read as a boundary, as indicated in the example described earlier.
When the robot identifies that a dynamic area involves an area
larger than would be created by people, pets or moving objects in
relative proportion with the former, then the updating of the map
may be suspended.
[0073] FIG. 13 depicts a flow of operation of a system as depicted
in FIG. 1, with the variant that tilt of the robot is detected and
addressed in software. At block 1301, the data acquisition system
generates data regarding the robot's physical environment, yielding
the generated data at block 1302. At block 1303, the generated data
is used to generate or update the map of the robot's physical
environment. At block 1304, a check is made to see if any elements
of the map (e.g. a map boundary area) has shifted beyond a
threshold limit. If not, then at block 1305, map generation or
update continues. However, if at block 1304 there has been a shift
beyond the threshold limit, then at block 1306, the map generation
is suspended, or the map is modified. In this aspect, the
instruction to suspend or modify is generated within the processing
apparatus, and does not originate from the sensing unit. After
either block 1305 or block 1306, flow returns to data generation,
so that further checks can be made to see whether the map elements
have returned to within threshold limits.
[0074] It should be noted that instructions to suspend or modify
the use of generated data for mapping need not come solely from the
sensing unit or from within the processing apparatus. These
respective features of the system depicted in FIG. 1 may operate
concurrently.
[0075] Detection of motion may rely on spatial scanning done by,
for example, a laser rangefinder, which may continuously scan a
robot's surroundings. When scanning indicates that consecutive
distance readings show "dynamic" movement, the spatial distance
represented by an aggregate distance, or by a distance
differential, may be compared to a pre-defined threshold value. If
the difference between the first to the last distance measurement
is larger than the threshold, it may be concluded that the robot is
tilted. FIG. 14 provides an example of such a scenario. Consider
the robot at location A moving through a room and passing a doorway
into an adjoining room. Assume that the robot employs a planar
spatial sensor enabling it to delineate the physical limits of its
surroundings. Such a sensor likely would detect, through the open
doorway, some portion of the wall of the adjoining room, which, in
the example case, may yield the detected length of wall segment B.
If one side of the advancing robot encounters an obstacle such as,
for example, a thick rug, that results in the robot straddling the
object (e.g., the left wheel(s) may be raised by the rug while the
right wheel(s) continues to roll on the floor), then the robot's
sensing plane likely will tilt toward its right side. Depending on
room geometry and degree of tilt, it is possible that the portion
of the sensing plane that had been detecting the wall of the
adjoining room at B, now would intersect the floor of the adjoining
room at the much closer location of B'. In such a case, as the
robot updates the map of its surroundings, the data may show the
wall boundary shift suddenly from B to B' while other boundaries
might show little or no variation in position. For a robot
monitoring sudden changes in consecutive cells--from empty cells at
B' during level operation to occupied cells at B' when the robot is
tilting--the determination that a tilt event has occurred may be
based on a comparison between the physical length represented by
the consecutive, newly-"occupied" cells and a pre-defined
threshold. If the represented distance, or distance differential,
meets or exceeds the threshold, it may be concluded that the robot
has tilted and map updating may be suspended.
4. Tilt as Detected and Addressed in Hardware
[0076] Detection of tilt in hardware may involve the use of an
accelerometer or similar component that may detect changes in the
orientation of the component's mounting surface.
[0077] With this approach, data generated by the spatial scanner
may be supplemented by data regarding changes in orientation. With
this latter data set providing contextual verification for the
spatial sensor's data, information collected while the
tilt-detecting component indicates that the spatial sensor has lost
its preferred orientation could be discarded. In a typical
embodiment, this data may be discarded before it is processed by
any localization or mapping software.
[0078] As depicted in FIG. 15, a robot uses a sensor generating 2D
spatial information in a horizontal plane from the robot's
surroundings. The dotted line indicates the sensing perimeter,
created by the spatial sensing plane intersecting objects
surrounding the robot. This perimeter informs the robot of nearby
obstacles and the boundaries presented by walls and doors.
[0079] As depicted in FIG. 16. if the robot traverses a low
obstacle, such as the door frame shown in FIG. 16, or an uneven
surface, then the robot may lose its parallel disposition with
respect to the floor. As a result, a sensor fixed to the robot
collecting spatial information regarding the robot's surroundings
may collect data at an angle away from horizontal. The dotted line
in FIG. 16 shows the intersection of the spatial sensor's plane of
detection with object surfaces surrounding the robot. With the
robot tilted, the generated spatial data becomes erroneous. The
calculated distance to the wall in front of the robot becomes
distorted as the detection plane at B' intersects the wall at a
higher point, but, more critically, the detection plane's
intersection with the floor behind the robot would incorrectly
report a linear boundary at A'.
[0080] Several features and aspects of the present invention have
been illustrated and described in detail with reference to
particular embodiments by way of example only, and not by way of
limitation. Reference herein to "one embodiment" or "an embodiment"
means that a particular feature, structure, operation, or other
characteristic described in connection with the embodiment may be
included in at least one implementation of the invention. However,
the appearance of the phrase "in one embodiment" or "in an
embodiment" in various places in the specification does not
necessarily refer to the same embodiment. It is envisaged that the
ordinarily skilled person could use any or all of the above
embodiments individually, or in any compatible combination or
permutation. Those of skill in the art will appreciate that
alternative implementations and various modifications to the
disclosed embodiments are within the scope and contemplation of the
present disclosure. Therefore, it is intended that the invention be
considered as limited only by the scope of the appended claims.
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