Calibration for autonomous vehicle operation

Patent metadata

Field	Value
Publication number	US-9916703-B2
Application number	US-201514756996-A
Country	US
Kind code	B2
Filing date	Nov 4, 2015
Priority date	Nov 4, 2015
Publication date	Mar 13, 2018
Grant date	Mar 13, 2018

Abstract

Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide an autonomous vehicle fleet as a service. In particular, a method may include receiving data associated with a sensor measurement of a perceived object, determining a label associated with the perceived object based on an initial calibration, retrieving log file data associated with the label, determining a calibration parameter associated with the sensor measurement based on the retrieved log file data, and storing the calibration parameter in association with a sensor associated with the sensor measurement. Sensors may be calibrated on the fly while the autonomous vehicle is in operation using one or more other sensors and/or fused data from multiple types of sensors.

First claim

What is claimed:

1. A method comprising:
receiving data at an autonomous vehicle system comprising a plurality of sensors, the plurality of sensors including an inertial measurement unit (IMU) sensor, an odometry sensor, and a multi-beam LIDAR sensor, and the data representing measurements made using the plurality of sensors;
determining a distance traveled by the autonomous vehicle system based on one or more sensor measurements of the odometry sensor;
determining a velocity of the autonomous vehicle system based on one or more sensor measurements of the IMU sensor;
identifying an abnormal sensor measurement, made using the multi-beam LIDAR sensor, based at least in part on the data;
generating an initial calibration parameter, associated with the multi-beam LIDAR sensor, based at least in part on the abnormal sensor measurement;
generating an expected sensor measurement, associated with the multi-beam LIDAR sensor, based on the data;
modifying the initial calibration parameter based at least in part on the expected sensor measurement, wherein modifying the initial calibration parameter includes generating a modified calibration parameter associated with the multi-beam LIDAR sensor; and
calibrating the multi-beam LIDAR sensor based at least in part on the modified calibration parameter, the distance traveled by the autonomous vehicle system, and the velocity of the autonomous vehicle system.

2. The method of claim 1 , wherein the plurality of sensors includes an array of LIDAR sensors arranged at one end of the autonomous vehicle system,
the array including the multi-beam LIDAR sensor.

3. The method of claim 2 , further comprising:
determining an extrinsic calibration of the multi-beam LIDAR sensor based on a physical location of the multi-beam LIDAR sensor relative to the autonomous vehicle system.

4. The method of claim 2 , further comprising:
determining an intrinsic calibration of the multi-beam LIDAR sensor based on data associated with at least one additional LIDAR sensor of the array disposed adjacent to the multi-beam LIDAR sensor.

5. The method of claim 1 , further comprising:
retrieving one or more log files, including information indicative of past sensor measurements, based on the abnormal sensor measurement; and
generating the expected sensor measurement based, in part, on the information indicative of past sensor measurements.

6. The method of claim 1 , wherein the expected sensor measurement is further based at least in part on a teleoperation interface selection made via a teleoperator system.

7. The method of claim 1 , wherein the expected sensor measurement is further based at least in part on a map tile associated with a pose of the autonomous vehicle system at a time of the abnormal sensor measurement.

8. The method of claim 1 , wherein the initial calibration parameter is generated based at least in part on a first probabilistic model, the method further comprising:
generating a second probabilistic model; and
generating an additional expected sensor measurement, associated with the multi-beam LIDAR sensor, using the second probabilistic model, wherein the additional expected sensor measurement is generated based on a measurement made using at least one additional sensor of the plurality of sensors.

9. A method comprising:
receiving, at an autonomous vehicle system comprising a plurality of sensors, a plurality of sensor measurements associated with an environmental feature, the plurality of sensors comprising an inertial measurement unit (IMU) sensor, an odometry sensor, and a multi-beam LIDAR sensor;
determining a distance traveled by the autonomous vehicle system based on one or more sensor measurements of the odometry sensor;
determining a velocity of the autonomous vehicle system based on one or more sensor measurements of the IMU sensor;
identifying an abnormal sensor measurement of the plurality of sensor measurements made using the multi-beam LIDAR sensor, wherein the abnormal sensor measurement is incongruent with one or more other measurements of the plurality of sensor measurements made using the multi-beam LIDAR sensor;
determining a plurality of calibration parameters associated with the multi-beam LIDAR sensor, wherein at least one parameter of the plurality of calibration parameters is determined based on the abnormal sensor measurement and a statistical computation associated with the environmental feature;
determining an expectation model associated with the multi-beam LIDAR sensor, wherein the expectation model is configured to output one or more expected sensor measurements;
generating an expected sensor measurement with the expectation model and using at least one measurement of the plurality of sensor measurements made using the multi-beam LIDAR sensor;
modifying the plurality of calibration parameters based at least in part on the expected sensor measurement, wherein modifying the plurality of calibration parameter includes generating a modified calibration parameter associated with the multi-beam LIDAR sensor; and
calibrating the multi-beam LIDAR sensor based at least in part on the modified calibration parameter, the distance traveled by the autonomous vehicle system, and the velocity of the autonomous vehicle system.

10. The method of claim 9 , further comprising:
comparing the plurality of sensor measurements to a predetermined sensor measurement range; and
identifying the abnormal sensor measurement based at least in part on the comparing.

11. The method of claim 9 , wherein the abnormal sensor measurement is incongruent with the one or more other measurements of the plurality of sensor measurements based on the abnormal sensor measurement not matching the one or more other measurements of the plurality of sensor measurements.

12. The method of claim 9 , wherein the statistical computation associated with the environmental feature comprises an average of the plurality of sensor measurements associated with the environmental feature.

13. The method of claim 9 , wherein the statistical computation associated with the environmental feature comprises a median of the plurality of sensor measurements associated with the environmental feature.

14. The method of claim 9 , wherein modifying the plurality of calibration parameters comprises:
determining a heuristic rule to identify a subset of calibration parameters based on the expectation model and the plurality of calibration parameters;
identifying the subset of calibration parameters using the heuristic rule; and
replacing the plurality of calibration parameters with the subset of calibration parameters.

15. A method comprising:
receiving, at an autonomous vehicle system comprising a plurality of sensors, data associated with a measurement made by a multi-beam LIDAR sensor of the plurality of sensors, wherein the measurement indicates detection of an object by the multi-beam LIDAR sensor, and the plurality of sensors further comprises at least one RADAR sensor;
determining a current physical location of the multi-beam LIDAR sensor, relative to the autonomous vehicle system, using the at least one RADAR sensor;
comparing the current physical location to a stored indication of an initial position of the multi-beam LIDAR sensor;
determining a labeled data point associated with the object, wherein the labeled data point is determined based at least in part on an initial calibration of the multi-beam LIDAR sensor;
retrieving log file data associated with the labeled data point, the log file data comprising one or more contradictory sensor measurements from the multi-beam LIDAR sensor;

Assignees

• Zoox Inc

Inventors

• Levinson Jesse Sol
• Douillard Bertrand Robert
• Sibley Gabriel Thurston

Classifications

• B60Q1/28

  for indicating front of vehicle · CPC title

• G08G1/005

  including pedestrian guidance indicator · CPC title

• G07C5/0841

  Registering performance data (recording measured values G01D; information storage G11B) · CPC title

• G01S15/931

  of land vehicles · CPC title

• G08G1/207

  with respect to certain areas, e.g. forbidden or allowed areas with possible alerting when inside or outside boundaries · CPC title