Adaptive mapping with spatial summaries of sensor data

• US 9,952,053 B2
• Filed: 08/01/2016
• Issued: 04/24/2018
• Est. Priority Date: 09/30/2011
• Status: Active Grant

• Current Assignee: iRobot Corporation
• Original Assignee: iRobot Corporation
• Inventors: Fong, Philip, Eade, Ethan, Munich, Mario E.
• Primary Examiner(s): Goldman, Richard A
• Application Number:
  US15/225,158
• Publication Number:
  US 20170052033A1
• Time in Patent Office: 631 Days
• Field of Search: 701400, 701409, 701445, 701446, 701447, 701448, 701450, 701451, 701 23, 701 25, 701 26, 701517, 701532, 700259, 700253, 700258, 702150, 702151, 702152
• CPC Class Codes: G01C 21/20 Instruments for performing navigational calculations G01C21/24, G01C21/26 take precedence
• CPC Class Codes: G01C 21/206 specially adapted for indoor navigation
• CPC Class Codes: G01C 21/3881 Tile-based structures
• CPC Class Codes: G05B 15/00 Systems controlled by a computer G05B13/00, G05B19/00 take precedence; automatic controllers with particular characteristics G05B11/00
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0238 using obstacle or wall sensors G05D1/0246 and G05D1/0289 take precedence; lidar systems designed for anti-collision purposes G01S17/93
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: Y10S 901/00 Robots

Abstract

A system and method for mapping parameter data acquired by a robot mapping system is disclosed. Parameter data characterizing the environment is collected while the robot localizes itself within the environment using landmarks. Parameter data is recorded in a plurality of local grids, i.e., sub-maps associated with the robot position and orientation when the data was collected. The robot is configured to generate new grids or reuse existing grids depending on the robot'"'"'s current pose, the pose associated with other grids, and the uncertainty of these relative pose estimates. The pose estimates associated with the grids are updated over time as the robot refines its estimates of the locations of landmarks from which determines its pose in the environment. Occupancy maps or other global parameter maps may be generated by rendering local grids into a comprehensive map indicating the parameter data in a global reference frame extending the dimensions of the environment.

Claims

1. A method of mapping an environment, the method comprising:

estimating a first current pose of a robot driving in an environment based on parameters measured by the robot, the robot having a visual sensor and the parameters including obstacles and clear spaces;

defining a first local origin that represents an estimate of the first current pose, wherein the first local origin is one of a plurality of local origins;

generating a first map of the measured parameters, wherein the measured parameters are mapped relative to the first pose;

after driving a determined period of time, determining an estimate of a second current pose of the robot;

determining an uncertainty between the estimate of the first current robot pose and the estimate of the second current pose of the robot; and

responsive to the uncertainty being greater than a first threshold, then;

defining a second local origin that represents the estimate of the second current pose of the robot; and

generating a second map of measured parameters mapped relative to the second current pose.

2. The method according to claim 1, wherein the visual sensor is configured to generate image data that includes features for recognizing a landmark in the environment.

3. The method according to claim 1,

wherein the first map comprises a first sub-map and the second map comprises a second sub-map, and

wherein the method further comprises generating a global map that includes data corresponding to the first sub-map and data corresponding to the second sub-map.

4. The method according to claim 1, wherein estimating the first current pose of the robot based on parameters measured by the robot is performed using sensor data that is generated by a plurality of sensors, the sensor data including data corresponding to the identification of obstacles and clear spaces that are included on the first map.

5. The method according to claim 1, wherein the first map and/or the second map include three-dimensional map data.

6. The method according to claim 1, after generating the first map and the second map, the method further comprising updating the first map and/or the second map based on defining the first local origin and/or the second local origin that are defined during robot travels occurring after the first map and the second map are generated.

7. The method according to claim 1, wherein the robot comprises a first robot, the method further comprising, after generating the first map and the second map, updating the first map and/or the second map based on map information that is generated by a second robot that is different from the first robot.

8. The method according to claim 1, further comprising merging, by the robot, system parameter data from a plurality of maps into a spatial summary.

9. The method according to claim 8,

wherein the merging is performed responsive to one or more of:

elapsed time, space covered by the robot or area mapped by the robot, a map memory limitation, or total number of maps or local origins.

10. The method according to claim 9, wherein the first local origin coincides with a starting position of the robot.

11. The method according to claim 1, wherein locations of obstacles detected by the robot within the environment comprise obstacles that are detected by a bump sensor.

12. The method according to claim 1, further comprising generating a spatial summary that is based on the measured parameters corresponding to the first map and the second map and that is associated with a local origin that is different from the first local origin and the second local origin.

13. The method according to claim 1, wherein the first local map comprises a map of local parameter data located relative to the first local origin, wherein the first local origin represents an estimate of the first current pose of the robot at a location.