Control device and control method, program, and mobile object

Patent metadata

Field	Value
Publication number	US-11592829-B2
Application number	US-201816767593-A
Country	US
Kind code	B2
Filing date	Nov 21, 2018
Priority date	Dec 5, 2017
Publication date	Feb 28, 2023
Grant date	Feb 28, 2023

Abstract

A control device and a control method can quickly estimate a self-location even when the self-location is unknown. In a case of storing information supplied in a time series detected by LIDAR or a wheel encoder and estimating a self-location by using the stored time-series information, when a position change happens unpredictably in advance such as a kidnap state is detected, the stored time-series information is reset, and then the self-location is estimated again. Example host platforms include a multi-legged robot, a flying object, and an in-vehicle system that autonomously moves in accordance with a mounted computing machine.

First claim

The invention claimed is:

1. A control device comprising:
a computer having a central processing unit configured by execution of computer readable instructions to implement
a self-position detector that detects a self-position of the control device on a basis of sensor information applied to the computer;
a position change detector that detects a position change of the control device that is unpredictable in advance on a basis of a detection result by the self-position detector; and
a self-location estimator that is configured to
estimate a self-location of the control device with a first estimation model on a basis of the sensor information; and
estimate a self-location of the control device with a second estimation model different from the first estimation model under a condition that the position change that is unpredictable in advance is detected by the position change detector;

wherein the self-location estimator includes
a time-series information accumulator that accumulates the sensor information therein as time-series information;
a time-series information self-location estimator configured to
use time-series information accumulated in the time-series information accumulator to estimate the self-location of the control device, and
output an estimation result as a time-series information self-location,

wherein in the time-series information accumulator, the time-series information of a past time that has been accumulated is reset in a case where the position change that is unpredictable in advance is detected, and
the time-series information self-location estimator
uses time-series information accumulated in the time-series information accumulator to estimate the self-location, as the first estimation model; and
uses time-series information accumulated in the time-series information accumulator after the time-series information of a past time that has been accumulated in the time-series information accumulator is reset, to estimate the self-location, as the second estimation model.

2. The control device according to claim 1 , wherein
by the reset, the time-series information of a past time that has been accumulated in the time-series information accumulator is deleted, in an order from time-series information accumulated longer than a predetermined time.

3. The control device according to claim 1 , wherein
the time-series information self-location estimator reduces a weight of a self-location estimated with the time-series information as the time-series information has been accumulated for a longer time in the time-series information accumulator after the reset is performed on time-series information of a past time that has been accumulated in the time-series information accumulator, to estimate the time-series information self-location.

4. The control device according to claim 1 , wherein the self-location estimator includes
a time-series information self-location estimator configured to estimate a time-series information self-location by using a Kalman filter that repeats a step of updating an observation value of a prior probability density distribution with time-series information that is latest, wherein
the time-series information self-location estimator
estimates the time-series information self-location by using the Kalman filter that repeats a step of updating an observation value of a prior probability density distribution with time-series information that is latest, as the first estimation model, and
estimates the time-series information self-location by using the Kalman filter after maximizing a weight of the time-series information that is latest, as the second estimation model, in a case where the position change that is unpredictable in advance is detected.

5. The control device according to claim 1 , wherein the self-location estimator includes:
a time-series information accumulator that accumulates as time-series information sensed with the sensor information;
a time-series information self-location estimator configured to use time-series information accumulated in the time-series information accumulator to estimate the self-location, and output an estimation result as a time-series information self-location; and
a current information self-location estimator configured to estimate the self-location on a basis of current information that is current information sensed with the sensor information, and output as a current information self-location,
wherein the self-location estimator
adopts the time-series information self-location as a self-location estimation result in a case where the position change that is unpredictable in advance is not detected, and
adopts the current information self-location as a self-location estimation result in a case where the position change that is unpredictable in advance is detected.

6. The control device according to claim 1 , wherein the self-location estimator
causes transition of an operation mode to at least a first mode, a second mode, and a third mode in accordance with a detection result of the position change detector;
sets the operation mode to the first mode at a start of an operation and estimates the self-location with the first estimation model;
causes, in the first mode, transition of the operation mode to the second mode and notifies that the position change that is unpredictable in advance has been detected in a case where the position change that is unpredictable in advance is detected by the position change detector;
causes, in the second mode, transition of the operation mode to the third mode after a predetermined time has elapsed, and estimates the self-location with the second estimation model; and
causes, in the third mode, transition of the operation mode to the first mode after a predetermined time has elapsed.

7. The control device according to claim 6 , wherein the self-location estimator includes:
a time-series information accumulator that accumulates the sensor information as time-series information;
a time-series information self-location estimator configured to use time-series information accumulated in the time-series information accumulator to estimate the self-location, and output an estimation result as a time-series information self-location; and
a current information self-location estimator configured to
estimate the self-location on a basis of current information that is current information sensed with the sensor information;
output as a current information self-location;
adopts, in the first mode, as a self-location estimation result, the time-series information self-location estimated by the time-series information self-location estimator with use of time-series information accumulated in the time-series information accumulator, as the first estimation model; and
adopts, in the third mode, as a self-location estimation result, the current information self-location estimated by the current information self-location estimator with use of the current information, as the second estimation model.

8. The control device according to claim 7 , wherein
the position change that is unpredictable in advance includes a change to a state in which a change in the self-location and a position loses continuity, and
the first mode includes a normal mode, the second mode includes an uncertain self-location mode, and the third mode includes a current information self-location estimation mode.

9. The control device according to claim 8 , wherein
in a case where the operation mode is the current information self-location estimation mode that is the third mode, when the position change that is unpredictable in advance is detected, the self-location estimator causes transition of the operation mode to the

Assignees

• Sony Corp

Inventors

• Kobayashi Dai
• Watanabe Ryo

Classifications

• G05D2101/10

  using artificial intelligence [AI] techniques · CPC title

• G05D2109/12

  with legs · CPC title

• G06N3/08

  Learning methods · CPC title

• G05D1/247

  using signals provided by artificial sources external to the vehicle, e.g. navigation beacons · CPC title

• G05D1/242

  Means based on the reflection of waves generated by the vehicle (using passive navigation aids external to the vehicle G05D1/244; using signals provided by artificial sources external to the vehicle G05D1/247) · CPC title