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US20110082585A1 - Method and apparatus for simultaneous localization and mapping of mobile robot environment - Google Patents

Method and apparatus for simultaneous localization and mapping of mobile robot environment Download PDF

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Publication number
US20110082585A1
US20110082585A1 US12/873,018 US87301810A US2011082585A1 US 20110082585 A1 US20110082585 A1 US 20110082585A1 US 87301810 A US87301810 A US 87301810A US 2011082585 A1 US2011082585 A1 US 2011082585A1
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United States
Prior art keywords
robot
physical environment
particles
map
data acquisition
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Abandoned
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US12/873,018
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English (en)
Inventor
Boris Sofman
Vladimir Ermakov
Mark Emmerich
Steven Alexander
Nathaniel David MONSON
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Neato Robotics Inc
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Neato Robotics Inc
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Publication date
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Priority to US12/873,018 priority Critical patent/US20110082585A1/en
Assigned to NEATO ROBOTICS, INC. reassignment NEATO ROBOTICS, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: ERMAKOV, VLADIMIR, SOFMAN, BORIS, MONSON, NATHANIEL DAVID, ALEXANDER, STEVEN, EMMERICH, MARK
Publication of US20110082585A1 publication Critical patent/US20110082585A1/en
Priority to US14/067,705 priority patent/US8903589B2/en
Assigned to SQUARE 1 BANK reassignment SQUARE 1 BANK SECURITY AGREEMENT Assignors: NEATO ROBOTICS, INC.
Priority to US14/543,508 priority patent/US9678509B2/en
Assigned to NEATO ROBOTICS, INC. reassignment NEATO ROBOTICS, INC. RELEASE BY SECURED PARTY (SEE DOCUMENT FOR DETAILS). Assignors: SQUARE 1 BANK
Priority to US15/602,012 priority patent/US20170255203A1/en
Abandoned legal-status Critical Current

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
    • G05D1/0274—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means using mapping information stored in a memory device
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D3/00—Control of position or direction
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J11/00—Manipulators not otherwise provided for
    • B25J11/008—Manipulators for service tasks
    • B25J11/0085—Cleaning
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/0003—Home robots, i.e. small robots for domestic use
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
    • G05D1/024—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors in combination with a laser
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S901/00—Robots
    • Y10S901/01—Mobile robot
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S901/00—Robots
    • Y10S901/46—Sensing device
    • Y10S901/47—Optical

Definitions

  • 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.
  • simultaneous localization and mapping 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.
  • 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.
  • FIG. 1 depicts a block diagram showing some features according to the invention.
  • FIG. 2 depicts a flow chart showing some features according to the invention, corresponding to certain aspects of FIG. 1 .
  • FIG. 3 depicts another flow chart showing some other features according to the invention, corresponding to certain aspects of FIG. 1 .
  • FIG. 4 depicts a block diagram showing some other features according to the invention.
  • FIG. 5 depicts an example of particle weight distribution for a localization iteration process.
  • FIG. 6 depicts a further example of particle weight distribution for a localization iteration process.
  • FIG. 7 depicts yet a further example of particle weight distribution for a localization iteration process.
  • FIG. 8 depicts localized and delocalized states based on verified particle distribution data.
  • FIG. 9 depicts a block diagram showing some other features according to the invention.
  • FIG. 10 depicts a flow chart showing some other features of the invention, corresponding to certain aspects of FIG. 6 .
  • FIG. 11 depicts an example of orientation of a mobile robot in its physical environment.
  • FIG. 12 depicts a further example of orientation of a mobile robot in its physical environment.
  • FIG. 13 depicts a flow chart showing some other features of the invention, corresponding to certain aspects of FIG. 1 .
  • FIG. 14 depicts one scenario of movement and orientation of a mobile robot in its physical environment.
  • FIG. 15 depicts a further scenario of movement and orientation of a mobile robot in its physical environment.
  • FIG. 16 depicts yet a further scenario of movement and orientation of a mobile robot in its physical environment.
  • 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.
  • 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.
  • FIG. 1 is a diagram depicting aspects of the just-mentioned feature in a mobile robotic system 100 .
  • data acquisition system 110 generates data regarding the environment of mobile robot 120 .
  • This data becomes input data to processing apparatus 130 .
  • 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.
  • FIG. 2 shows a flow of operation of the system depicted in FIG. 1 .
  • the data acquisition system generates data regarding the robot's physical environment, yielding the generated data at block 202 .
  • 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.
  • the generated data is used to generate or update the map of the robot's physical environment.
  • FIG. 3 is a diagram showing other features of the invention.
  • 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.
  • 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.
  • the method may operate as follows:
  • FIG. 4 depicts a flow of operation of the system depicted in FIG. 3 .
  • the existing map may be used or updated as appropriate.
  • particles are generated, either anew or iteratively, the iteratively generated particles being added to the existing particle set.
  • weights are assigned to each particle.
  • erroneous particles are generated, and at block 405 , the erroneous particles have weights assigned to them.
  • the weights of the erroneous particles are compared to those of the original particle set to determine whether delocalization has occurred.
  • 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.
  • a typical approach to localization under a SLAM scheme might include the following steps:
  • a typical localization iteration based on the above process might yield the particle weight distribution illustrated 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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°, 40°, 50°, 60°, etc.).
  • 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 .
  • 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.
  • 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.
  • 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.
  • 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.
  • both of these states, localized and delocalized, are depicted in the plots of the averaged verification particle data in FIG. 8 .
  • the plotted data are the averaged verification particle indices.
  • the averaged data are high and relatively constant, which is consistent with a localized state.
  • 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.
  • the return of the average to a high, stable number indicates that the robot likely recovered from the event.
  • 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.
  • 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.
  • the issue of distinguishing between static (permanent) elements of the robot's surroundings and dynamic (transient) elements may be addressed in the following way:
  • FIG. 9 is a diagram of a system containing other features of the invention.
  • 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.
  • 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.
  • FIG. 10 depicts a flow of operation of the embodiment shown in FIG. 9 .
  • the data acquisition system generates data regarding the robot's physical environment, yielding the generated data at block 1002 .
  • the generated data is used to generate or update the map of the robot's physical environment.
  • probabilities of occupancy for each cell in the grid map are assigned or updated.
  • 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.
  • 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.
  • the robot 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the data acquisition system generates data regarding the robot's physical environment, yielding the generated data at block 1302 .
  • the generated data is used to generate or update the map of the robot's physical environment.
  • 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.
  • the map e.g. a map boundary area
  • the instruction to suspend or modify is generated within the processing apparatus, and does not originate from the sensing unit.
  • flow returns to data generation, so that further checks can be made to see whether the map elements have returned to within threshold limits.
  • Detection of motion may rely on spatial scanning done by, for example, a laser rangefinder, which may continuously scan a robot's surroundings.
  • 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′.
  • an obstacle such as, for example, a thick rug
  • the data may show the wall boundary shift suddenly from B to B′ while other boundaries might show little or no variation in position.
  • 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.
  • 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.
  • data generated by the spatial scanner may be supplemented by data regarding changes in orientation.
  • 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.
  • this data may be discarded before it is processed by any localization or mapping software.
  • 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.
  • FIG. 16 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′.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Radar, Positioning & Navigation (AREA)
  • General Physics & Mathematics (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Remote Sensing (AREA)
  • Robotics (AREA)
  • Mechanical Engineering (AREA)
  • Optics & Photonics (AREA)
  • Electromagnetism (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
  • Manipulator (AREA)
US12/873,018 2009-08-31 2010-08-31 Method and apparatus for simultaneous localization and mapping of mobile robot environment Abandoned US20110082585A1 (en)

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US12/873,018 US20110082585A1 (en) 2009-08-31 2010-08-31 Method and apparatus for simultaneous localization and mapping of mobile robot environment
US14/067,705 US8903589B2 (en) 2009-08-31 2013-10-30 Method and apparatus for simultaneous localization and mapping of mobile robot environment
US14/543,508 US9678509B2 (en) 2009-08-31 2014-11-17 Method and apparatus for simultaneous localization and mapping of mobile robot environment
US15/602,012 US20170255203A1 (en) 2009-08-31 2017-05-22 Method and apparatus for simultaneous localization and mapping of mobile robot environment

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US12/873,018 US20110082585A1 (en) 2009-08-31 2010-08-31 Method and apparatus for simultaneous localization and mapping of mobile robot environment

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US14/543,508 Active US9678509B2 (en) 2009-08-31 2014-11-17 Method and apparatus for simultaneous localization and mapping of mobile robot environment
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US15/602,012 Abandoned US20170255203A1 (en) 2009-08-31 2017-05-22 Method and apparatus for simultaneous localization and mapping of mobile robot environment

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WO2014110204A1 (fr) * 2013-01-10 2014-07-17 Intel Corporation Positionnement et cartographie basés sur des points de repère virtuels
US20140350839A1 (en) * 2013-05-23 2014-11-27 Irobot Corporation Simultaneous Localization And Mapping For A Mobile Robot
US20150261223A1 (en) * 2011-09-30 2015-09-17 Irobot Corporation Adaptive mapping with spatial summaries of sensor data
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JP2013503404A (ja) 2013-01-31
CA2772636A1 (fr) 2011-03-03
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US9678509B2 (en) 2017-06-13
US20150105964A1 (en) 2015-04-16
KR101362961B1 (ko) 2014-02-12
CN104699099B (zh) 2018-03-23
HK1211352A1 (en) 2016-05-20
CA2859112C (fr) 2017-08-15
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JP2014078254A (ja) 2014-05-01
US20170255203A1 (en) 2017-09-07
WO2011026119A3 (fr) 2011-06-16
US8903589B2 (en) 2014-12-02
CA2859112A1 (fr) 2011-03-03
CN104699099A (zh) 2015-06-10
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US20140058610A1 (en) 2014-02-27

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