Simultaneous localization and mapping for a mobile robot

• US 9,400,501 B2
• Filed: 03/31/2015
• Issued: 07/26/2016
• Est. Priority Date: 11/02/2012
• Status: Active Grant

• Current Assignee: iRobot Corporation
• Original Assignee: iRobot Corporation
• Inventors: Schnittman, Mark
• Primary Examiner(s): Tran, Dalena
• Application Number:
  US14/674,427
• Publication Number:
  US 20150205299A1
• Time in Patent Office: 483 Days
• Field of Search: 700/253, 700/259, 700/245, 700/250, 382/106, 701/23, 701/26, 701/28, 701/409
• US Class Current: 1/1
• CPC Class Codes: G05D 1/0219 ensuring the processing of the whole working surface
• CPC Class Codes: G05D 1/0227 using mechanical sensing means, e.g. for sensing treated area
• CPC Class Codes: G05D 1/0242 using non-visible light signals, e.g. IR or UV signals
• CPC Class Codes: G05D 1/0272 comprising means for registering the travel distance, e.g. revolutions of wheels measuring distance traversed on the ground by vehicles, e.g. using odometers G01C22/00
• 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

Abstract

A method of simultaneous localization and mapping includes initializing a robot pose and a particle model of a particle filter. The particle model includes particles, each having an associated map, robot pose, and weight. The method includes receiving sparse sensor data from a sensor system of the robot, synchronizing the received sensor data with a change in robot pose, accumulating the synchronized sensor data over time, and determining a robot localization quality. When the accumulated sensor data exceeds a threshold accumulation and the robot localization quality is greater than a threshold localization quality, the method includes updating particles with accumulated synchronized sensor data. The method includes determining a weight for each updated particle of the particle model and setting a robot pose belief to the robot pose of the particle having the highest weight when a mean weight of the particles is greater than a threshold particle weight.

Claims

1. A method comprising:

receiving, at a computing device, sensor data from an autonomous mobile robot operating in a work environment;

updating, using the computing device, an occupancy map of the work environment with location occupancy probabilities based on the received sensor data;

determining, using the computing device, a localization quality of the autonomous mobile robot; and

when the localization quality does not satisfy a threshold localization quality, executing, using the computing device, a wall-following behavior that causes the autonomous mobile robot to maneuver toward a wall in the work environment and drive adjacent to the wall until the localization quality satisfies the threshold localization quality.

2. The method of claim 1, wherein the wall-following behavior uses proximity sensor data from a proximity sensor of the autonomous mobile robot to cause the autonomous mobile robot to maintain a threshold distance from the wall while driving adjacent the wall.

3. The method of claim 1, further comprising, when the localization quality does not satisfy the threshold localization quality, refraining from updating the occupancy map.

4. The method of claim 1, further comprising, when the localization quality does not satisfy the threshold localization quality:

identifying a location of the wall using the occupancy map; and

issuing a drive command from the computing device to a drive system of the autonomous mobile robot to maneuver the autonomous mobile robot toward the identified location of the wall.

5. The method of claim 4, further comprising:

after updating the occupancy map, storing the updated occupancy map in memory storage hardware in communication with the computing device; and

when the localization quality does not satisfy the threshold localization quality;

retrieving a previously stored occupancy map from the memory storage hardware; and

identifying the location of the wall using the previously stored occupancy map.

6. The method of claim 1, further comprising:

synchronizing the received sensor data with a change in robot pose;

accumulating the synchronized sensor data over time in memory storage hardware in communication with the computing device; and

when the received sensor data exceeds a threshold accumulation and the localization quality satisfies the threshold localization quality, updating particles of a particle model of a particle filter with the accumulated synchronized sensor data, each particle having an associated particle occupancy map, robot pose, and weight.

7. The method of claim 6, wherein the sensor data comprises range data from at least one range finding sensor of the autonomous mobile robot and the synchronized sensor data comprises range measurement and bearing pairs.

8. The method of claim 7, wherein the threshold accumulation of the sensor data comprises at least 20 range measurements from the at least one range finding sensor.

9. The method of claim 6, wherein determining the robot localization quality comprises determining an average weight of the particles of the particle model.

10. The method of claim 6, further comprising:

determining the weight for each updated particle of the particle model based on whether the received sensor data corroborates the corresponding particle occupancy map; and

setting a robot pose belief to the robot pose of the particle having a highest weight when a mean weight of the particles is greater than a threshold particle weight.

11. The method of claim 6, further comprising, when the localization quality does not satisfy the threshold localization quality, adding new particles to the particle model based on the sensor data received while executing the wall-following behavior.

12. The method of claim 6, further comprising:

sampling the particle model for a particle population; and

determining, using the computing device, an instantaneous localization of the autonomous mobile robot based on at least one particle of the particle population.

13. The method of claim 12, further comprising resampling the particle model for the particle population when the localization quality satisfies the threshold localization quality.

14. The method of claim 12, further comprising, when the localization quality does not satisfy the threshold localization quality, replacing a portion of the particle population with particles selected from the particle model based on the accumulated synchronized sensor data.

15. The method of claim 1, wherein determining the robot localization quality comprises:

computing an instantaneous localization based on the sensor data;

receiving the instantaneous localization through a fast low-pass filter;

receiving the instantaneous localization through a slow low-pass filter; and

receiving the instantaneous localization through a leaky integrator;

wherein the localization quality does not satisfy the threshold localization quality when an integrator value of the leaky integrator fails to satisfy an integrator threshold.

16. A method comprising:

receiving, at a computing device, sensor data from an autonomous mobile robot operating in a work environment;

updating, using the computing device, an occupancy map of the work environment with location occupancy probabilities based on the received sensor data;

determining, using the computing device, a localization quality of the autonomous mobile robot; and

when the localization quality does not satisfy a threshold localization quality;

identifying, using the computing device, a mapped object on the occupancy map; and

issuing a drive command from the computing device to a drive system of the autonomous mobile robot to maneuver the autonomous mobile robot toward the mapped object in the work environment to re-localize the autonomous mobile robot based on the sensor data.

17. The method of claim 16, further comprising:

comparing, using the computing device, the sensor data relative to the mapped object on the occupancy map; and

determining, using the computing device, a pose belief of the autonomous mobile robot based on the comparison.

18. The method of claim 16, further comprising, when the localization quality does not satisfy the threshold localization quality, refraining from updating the occupancy map.

19. The method of claim 16, further comprising:

after updating the occupancy map, storing the updated occupancy map in memory storage hardware in communication with the computing device; and

when the localization quality does not satisfy the threshold localization quality;

retrieving a previously stored occupancy map from the memory storage hardware; and

issuing the drive command based on the previously stored occupancy map.

20. The method of claim 16, further comprising:

synchronizing the received sensor data with a change in robot pose;

accumulating the synchronized sensor data over time in memory storage hardware in communication with the computing device; and

when the received sensor data exceeds a threshold accumulation and the localization quality satisfies the threshold localization quality, updating particles of a particle model of a particle filter with the accumulated synchronized sensor data, each particle having an associated particle occupancy map, robot pose, and weight.

21. The method of claim 20, wherein the sensor data comprises range data from at least one range finding sensor of the autonomous mobile robot and the synchronized sensor data comprises range measurement and bearing pairs.

22. The method of claim 21, wherein the threshold accumulation of the sensor data comprises at least 20 range measurements from the at least one range finding sensor.

23. The method of claim 20, wherein determining the robot localization quality comprises determining an average weight of the particles of the particle model.

24. The method of claim 20, further comprising:

determining the weight for each updated particle of the particle model based on whether the received sensor data corroborates the corresponding particle occupancy map; and

setting a robot pose belief to the robot pose of the particle having a highest weight when a mean weight of the particles is greater than a threshold particle weight.

25. The method of claim 20, further comprising, when the localization quality does not satisfy the threshold localization quality, adding new particles to the particle model based on the sensor data received while the autonomous mobile robot maneuvers toward the mapped object in the work environment.

26. The method of claim 20, further comprising:

sampling the particle model for a particle population; and

determining, using the computing device, an instantaneous localization of the autonomous mobile robot based on at least one particle of the particle population.

27. The method of claim 26, further comprising resampling the particle model for the particle population when the localization quality satisfies the threshold localization quality.

28. The method of claim 26, further comprising, when the localization quality does not satisfy the threshold localization quality, replacing a portion of the particle population with particles selected from the particle model based on the accumulated synchronized sensor data.

29. The method of claim 16, wherein determining the robot localization quality comprises:

computing an instantaneous localization based on the sensor data;

receiving the instantaneous localization through a fast low-pass filter;

receiving the instantaneous localization through a slow low-pass filter; and

receiving the instantaneous localization through a leaky integrator;

wherein the localization quality does not satisfy the threshold localization quality when an integrator value of the leaky integrator fails to satisfy an integrator threshold.