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

• US 9,678,509 B2
• Filed: 11/17/2014
• Issued: 06/13/2017
• Est. Priority Date: 08/31/2009
• Status: Active Grant

• Current Assignee: Vorwerk & Company Interholding Gesellschaft Mit Beschrã¤Nkter Haftung (Vorwerk & Company KG)
• Original Assignee: Neato Robotics, Inc. (Vorwerk & Company KG)
• Inventors: Alexander, Steven, Monson, Nathaniel David, Emmerich, Mark, Sofman, Boris, Ermakov, Vladimir
• Primary Examiner(s): LIN, ABBY YEE
• Application Number:
  US14/543,508
• Publication Number:
  US 20150105964A1
• Time in Patent Office: 939 Days
• CPC Class Codes: B25J 11/0085 Cleaning
• CPC Class Codes: B25J 9/0003 Home robots, i.e. small robots for domestic use
• CPC Class Codes: B25J 9/1602 characterised by the control system, structure, architecture
• CPC Class Codes: G05D 1/024 in combination with a laser lasers per se H01S
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: Y10S 901/01 Mobile robot
• CPC Class Codes: Y10S 901/47 Optical

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.

Claims

1. 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 and generate spatial data indicating distances to boundaries and objects from a current position of the mobile device;

a map generator mounted on the mobile device and 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.

2. A mobile device tracking system as claimed in claim 1, wherein the erroneous particle generator selects erroneous particles so as to avoid introduction of additional error into the current estimate of the current position.

3. A mobile device tracking system as claimed in claim 1, wherein the mobile device is a robot.

4. A mobile device tracking system as claimed in claim 1, 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.

5. A mobile device tracking system as claimed in claim 1, wherein the erroneous particles comprise erroneous position particles indicating position of the current position of the mobile device.

6. A mobile device tracking system as claimed in claim 5, wherein the particle weight comparator is further configured to determine the accuracy of the current position.

7. A mobile device tracking system as claimed in claim 1, wherein the erroneous position particles comprise erroneous inclination particles indicating orientation of the mobile device.

8. A mobile device tracking system as claimed in claim 7, wherein the particle weight comparator is further configured to determine the accuracy of the orientation of the mobile device.

9. A mobile device tracking system as claimed in claim 8, wherein the particle weight comparator is further configured to determine when a tilt event has occurred.

10. A method for controlling movement of a mobile device, the method comprising:

tracking, with a sensor, movement of the mobile device;

generating, with a map generator, a map of the mobile device'"'"'s physical environment, said generating occurring with one of concurrently with and prior to tracking the current position of the mobile device;

updating, with the map generator, said map with one of concurrently with and prior to tracking the mobile current position;

estimating, with a delocalization detector, the mobile current position within said map by generating position particles, each of said position particles representing one of a potential position and orientation of the mobile device within its physical environment, wherein a data set of said position particles is generated and maintained iteratively to track changing position of the mobile device within its physical environment, said estimating including;

i. assigning a weight to each position particle, the particle weight being a relative measure of the likelihood of the position particle accurately representing the current mobile position with respect to other position particles; and

ii. introducing erroneous particles having weights that are uniformly low with respect to the weights of the position particles; and

iii. comparing the weights of the erroneous particles and the weights of the position particles to determine 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 and controlling movement of the mobile device in accordance with the steps of tracking, updating, and estimating.

11. A method as claimed in claim 10, wherein the erroneous particles are selected so as to avoid introduction of additional error into the current estimate of the position of the mobile device.

12. A method as claimed in claim 10, wherein the mobile device is a robot.

13. A method as claimed in claim 10, wherein estimating the current position includes calculating and monitoring the averaged weight or the median weight of the erroneous particles over multiple iterations.

14. A method as claimed in claim 10, wherein the erroneous particles comprise erroneous position particles indicating position of the mobile device.

15. A method as claimed in claim 14, wherein estimating the current position includes determining the accuracy of the current position.

16. A method as claimed in claim 10, wherein the erroneous position particles comprise erroneous inclination particles indicating inclination of the mobile device.

17. A method as claimed in claim 16, wherein estimating the current position includes determining accuracy of inclination of the mobile device.

18. A method as claimed in claim 17, wherein determining accuracy of inclination includes determining when a tilt event has occurred.