Adaptive mapping with spatial summaries of sensor data

• US 9,218,003 B2
• Filed: 06/17/2014
• Issued: 12/22/2015
• 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): Tarcza, Thomas
• Assistant Examiner(s): GOLDMAN, RICHARD A
• Application Number:
  US14/307,402
• Publication Number:
  US 20150261223A1
• Time in Patent Office: 553 Days
• Field of Search: None
• US Class Current: 1/1
• 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 mapping system configured to map parameters acquired by a robotic system in an environment, the mapping system comprising:

non-volatile memory configured to store computer-executable instructions; and

a hardware processor in communication with the non-volatile memory, the hardware processor configured to execute the computer-executable instructions to at least;

measure a first set of parameters that characterize the environment;

estimate a first current pose of the robotic system at a first location in the environment;

define a first anchor node representing the estimate of the first current pose;

generate a first grid associated with the first anchor node, wherein the first grid comprises a map of the first set of measured parameters, wherein the first set of measured parameters are mapped relative to the first pose;

determine an estimate of a second current pose of the robotic system at a second location in the environment;

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

if the uncertainty is greater than a first threshold, then;

generate a second grid associated with a second anchor node, the second anchor node representing the estimate of the second current pose of the robotic system, wherein the second grid comprises a map of a second set of measured parameters mapped relative to the second current pose.

2. The mapping system of claim 1, wherein the hardware processor is configured to execute the computer-executable instructions to merge grids associated with the plurality of anchor node by at least:

making a determination of an uncertainty between an estimate of each pose of the plurality of anchor nodes and estimates of other poses in the plurality of anchor nodes;

making a determination of an uncertainty between the estimates of two of the plurality of anchor nodes; and

combining the grids associated with the two anchor nodes if the uncertainty between estimates of poses of two anchor nodes is below a second threshold.

3. The mapping system of claim 2, wherein the uncertainty between estimates of poses is determined using a covariance matrix of relative pose estimates.

4. The mapping system claim 1, wherein the estimate of the first current pose is performed by making:

an identification of a plurality of landmarks in the environment;

a determination of locations of the plurality of landmarks with respect to a global reference frame associated with the environment;

a determination of the first current pose of the robotic system with respect to the global reference frame based at least in part on the locations of the plurality of landmarks.

5. The mapping system of claim 1, wherein the hardware processor is configured to execute the computer-executable instructions to:

identify a plurality of landmarks in the environment; and

determine locations of the plurality of landmarks.

6. The mapping system of claim 5, wherein the hardware processor is configured to execute the computer-executable instructions to:

update poses associated with the plurality of anchor nodes based on the locations of the plurality of landmarks; and

generate an occupancy map from a plurality of grids, wherein locations of the plurality of grids are based on locations of respective anchor nodes.

7. The mapping system of claim 1, wherein the first set of parameters being mapped comprises occupancy data.

8. The mapping system of claim 7, wherein the occupancy data indicates locations of obstacles in the environment.

9. The mapping system of claim 1, wherein the first set of parameters being mapped indicate the locations of dirt in the environment.

10. The mapping system of claim 1, further comprising a drive mechanism, including at least one form of locomotion, a motor, and a battery pack.

11. The mapping system of claim 1, further comprising a sensor processor, a parameter processor, a localization module, a parameter mapping module, and a navigation module.

13. The mapping system of claim 10, wherein the hardware processor is configured to execute the computer-executable instructions to:

render a plurality of the spatial summary grids into a global parameter map.

14. The mapping system of claim 11, wherein at least one of the spatial summary grids comprises a map of parameter data in proximity to a location given by an associated pose estimate.

16. The mapping system of claim 10, wherein the spatial summary grid consists of a single grid, wherein the single grid comprises parameter data from at least two grids associated with different pose estimates.

17. The mapping system of claim 14, wherein parameter data is represented in at least one spatial summary grid using one or more polygons.

18. The mapping system of claim 10, further comprising a drive mechanism, including at least one form of locomotion, a motor, and a battery pack.

19. The mapping system of claim 10, further comprising a sensor processor, a parameter processor, a localization module, a parameter mapping module, and a navigation module.

12. A mapping system configured to map parameters acquired by a robotic system in an environment, the mapping system comprising:

non-volatile memory configured to store computer-executable instructions; and

a hardware processor in communication with the non-volatile memory, the hardware processor configured to execute the computer-executable instructions to at least;

measure parameter data that characterizes the environment;

map the measured parameter data to a first plurality of grids, wherein a given grid in the first plurality of grids is associated with a respective estimate from a plurality of pose estimates;

determine an uncertainty between a given pose estimate and other pose estimates in the plurality of pose estimates; and

if the uncertainty between two of the plurality of pose estimates is below a threshold, then combine the grids associated with the two pose estimates into a spatial summary grid associated with a corresponding pose estimate.

15. The mapping system of claim 12, wherein at least one of the spatial summary grids comprises a Cartesian coordinate system different than the global parameter map.