Localization and mapping system and method for a robotic device

• US 20070061043A1
• Filed: 09/01/2006
• Published: 03/15/2007
• Est. Priority Date: 09/02/2005
• 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: Augenbraun, Joe, Woodward, Mark, Ermakov, Vladimir
• Granted Patent: US 8,483,881 B2
• Time in Patent Office: Days
• US Class Current: 700/263
• CPC Class Codes: A47L 2201/00 Robotic cleaning machines, i.e. with automatic control of the travelling movement or the cleaning operation
• CPC Class Codes: A47L 2201/04 Automatic control of the travelling movement; Automatic obstacle detection
• CPC Class Codes: A47L 5/225 Convertible suction cleaners, i.e. convertible between different types thereof, e.g. from upright suction cleaners to sledge-type suction cleaners
• CPC Class Codes: A47L 9/009 Carrying-vehicles; Arrangements of trollies or wheels; Means for avoiding mechanical obstacles
• CPC Class Codes: B25J 9/0003 Home robots, i.e. small robots for domestic use
• CPC Class Codes: B25J 9/1664 characterised by motion, path, trajectory planning
• CPC Class Codes: B25J 9/1679 characterised by the tasks executed
• CPC Class Codes: G05B 2219/36513 Select out of a plurality of programs, patterns
• CPC Class Codes: G05B 2219/40095 Modify tasks due to modular tooling, other fixture configuration, environment
• CPC Class Codes: G05B 2219/40423 Map task space to sensor space
• CPC Class Codes: G05B 2219/40506 Self motion topology knowledge, configuration mapping
• CPC Class Codes: G05D 1/0225 involving docking at a fixed facility, e.g. base station or loading bay parking aids B62D15/027
• 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

Abstract

A multi-function robotic device may have utility in various applications. In accordance with one aspect, a multi-function robotic device may be selectively configurable to perform a desired function in accordance with the capabilities of a selectively removable functional cartridge operably coupled with a robot body. Localization and mapping techniques may employ partial maps associated with portions of an operating environment, data compression, or both.

Claims

1. A method of mapping an operating environment to facilitate navigation of a robot, said method comprising:

acquiring optical data at a scan point associated with a partial map representing a portion of the operating environment;

identifying features of the portion of the operating environment and associating identified features with said partial map;

traversing the portion of the operating environment represented by said partial map;

acquiring additional optical data; and

updating said partial map responsive to said traversing and said acquiring additional data.

2. The method of claim 1 further comprising selectively repeating said traversing, said acquiring additional data, and said updating.

3. The method of claim 1 further comprising exiting said partial map and proceeding to a scan point of a different partial map representing a different portion of the operating environment.

4. The method of claim 1 wherein said acquiring optical data and said acquiring additional optical data comprise utilizing a source of collimated light.

5. The method of claim 4 wherein said acquiring optical data and said acquiring additional optical data further comprise utilizing a source of diffuse light.

6. The method of claim 1 wherein said identifying and said updating comprise computing a distance to an identified feature responsive to the optical data and the additional optical data.

7. A method of mapping an operating environment to facilitate navigation of a robot, said method comprising:

segmenting the operating environment into a plurality of partial maps, each respective one of said plurality of partial maps representing a respective portion of the operating environment;

mapping a respective one of said plurality of partial maps by;

acquiring optical data to identify features associated with the respective portion of the operating environment;

responsive to said acquiring, associating identified features with said respective one of said plurality of partial maps;

selectively repeating said acquiring and said associating; and

exiting said respective one of said plurality of partial maps and proceeding to a different one of said plurality of partial maps representing a different portion of the operating environment;

and;

selectively repeating said mapping until each respective portion of the operating environment has been mapped.

8. The method of claim 7 wherein said acquiring optical data comprises utilizing a source of collimated light.

9. The method of claim 8 wherein said acquiring optical data further comprises utilizing a source of diffuse light.

10. The method of claim 7 wherein said associating comprises compressing a data structure maintaining data representative of said respective one of said plurality of partial maps.

11. A method of determining a localization of a robot within an operating environment, said method comprising:

segmenting the operating environment into a plurality of partial maps;

acquiring data related to a distance between the robot and a feature in a portion of the operating environment corresponding to the one of said plurality of partial maps currently occupied by the robot; and

computing a location of the robot in accordance with said acquiring.

12. The method of claim 11 further comprising selectively repeating said acquiring and said computing.

13. The method of claim 11 wherein said acquiring data comprises utilizing a source of collimated light.

14. The method of claim 11 wherein said acquiring data further comprises utilizing a source of diffuse light.

15. A robot comprising a computer-readable medium encoded with data and instructions for determining a location of the robot in an operating environment;

the data and instructions causing the robot to;

generate a plurality of particles, each of said plurality of particles having associated hypothesis data representing an hypothesis regarding a position and an inclination of the robot;

acquire data related to a perspective of the operating environment from a current location of the robot;

compare said data to said hypothesis data associated with at least some of said plurality of particles; and

apply a weight to at least some of said plurality of particles in accordance with the comparison of said data and said hypothesis data;

wherein a position component of said hypothesis data for each of said plurality of particles is restricted to a subset of locations in the operating environment.

16. The robot of claim 15 further encoded with data and instructions;

the data and instructions further causing the robot to;

increase a weight for one of said plurality of particles when said associated hypothesis data are similar to said data acquired by the robot; and

decrease a weight for one of said plurality of particles when said associated hypothesis data are not similar to said data acquired by the robot.

17. The robot of claim 16 further encoded with data and instructions;

the data and instructions further causing the robot to discard those of said plurality of particles having a weight below a discard threshold.

18. The robot of claim 16 further encoded with data and instructions;

the data and instructions further causing the robot to clone those of said plurality of particles having a weight above a clone threshold.