Sensor consensus monitor

Patent metadata

Field	Value
Publication number	US-11897486-B1
Application number	US-202117536940-A
Country	US
Kind code	B1
Filing date	Nov 29, 2021
Priority date	Nov 29, 2021
Publication date	Feb 13, 2024
Grant date	Feb 13, 2024

Abstract

Techniques described are related to determining when a discrepancy between data of multiple sensors (e.g., IMUs) might be attributable to a sensor error, as opposed to operating conditions, such as sensor bias or noise. For example, the sensor data is passed through one or more filters (e.g., bandpass filter) that model the bias or noise, and the filtered data may then be compared for consistency. In some examples, consistency may be based on residuals or some other metric describing discrepancy among the filtered sensor data.

First claim

What is claimed is:

1. A method comprising:
receiving first sensor data associated with a first sensor and second sensor data associated with a second sensor;
determining third sensor data by filtering high-frequency data and low-frequency data from the first sensor data and the second sensor data;
determining, based at least in part on the third sensor data, a discrepancy metric associated with the first sensor;
determining, based at least in part on the discrepancy metric exceeding a threshold metric, an error associated with the first sensor; and
using, based at least in part on the error associated with the first sensor, the second sensor data instead of the first sensor data to control an operation of a vehicle.

2. The method of claim 1 , wherein the first sensor and the second sensor include gyroscopes, accelerometers, pressure sensors, magnetometers, inertial measurement units, or any combination thereof.

3. The method of claim 1 further comprising:
receiving fourth sensor data associated with the first sensor, wherein the fourth sensor data is received prior to the first sensor data; and
controlling, based on the fourth sensor data, a second operation of the vehicle before the operation, wherein the operation includes recalibrating the first sensor.

4. The method of claim 1 , wherein:
the filtering of high-frequency data includes using a low-pass filter including one or more parameters configured to devalue sensor data corresponding at least in part to sensor noise; and
the low-pass filter includes one or more of:
an exponential filter,
a finite impulse response filter,
an infinite impulse response filter, or
an exponential decay filter.

5. The method of claim 1 , wherein:
the filtering of low-frequency data includes using a high-pass filter including one or more parameters configured to devalue sensor data corresponding at least in part to sensor bias; and
the high-pass filter includes one or more of:
an exponential filter,
a finite impulse response filter,
an infinite impulse response filter, or
an exponential decay filter.

6. The method of claim 1 , wherein the discrepancy metric includes a first residual that is associated with the first sensor and that is higher than a second residual associated with the second sensor.

7. The method of claim 1 , wherein:
the first sensor and the second sensor include a first inertial measurement unit (IMU) and a second IMU associated with respective positions relative to the vehicle;
the method further comprises:
determining, by transformation to a reference point associated with the vehicle, transformed sensor data based on the first sensor data and the second sensor data; and
the discrepancy metric is based on the transformed sensor data.

8. The method of claim 1 , wherein:
the first sensor data and the second sensor data include time series data; and
the method further comprises beginning to filter the high-frequency data at an earlier time in the time series data than a later time at which beginning to filter the low-frequency data.

9. One or more non-transitory computer-readable media storing instructions that, when executed, cause one or more processors to perform operations comprising:
receive first sensor data associated with a first sensor and second sensor data associated with a second sensor;
determine third sensor data by filtering high-frequency data and low-frequency data from the first sensor data and the second sensor data;
determine, based at least in part on the third sensor data, a discrepancy metric associated with the first sensor;
determine, based at least in part on the discrepancy metric exceeding a threshold metric, an error associated with the first sensor; and
use, based at least in part on the error associated with the first sensor, the second sensor data instead of the first sensor data to control an operation of a vehicle.

10. The one or more non-transitory computer-readable media of claim 9 , wherein the first sensor and the second sensor include gyroscopes, accelerometers, pressure sensors, magnetometers, inertial measurement units, or any combination thereof.

11. The one or more non-transitory computer-readable media of claim 9 , wherein operations further comprise:
receiving fourth sensor data associated with the first sensor, wherein the fourth sensor data is received prior to the first sensor data; and
controlling, based at least in part on the fourth sensor data, a second operation of the vehicle before the operation; and
changing, based at least in part on the error associated with the first sensor, from controlling the vehicle based on data generated by the first sensor to controlling the vehicle based on data generated by the second sensor.

12. The one or more non-transitory computer-readable media of claim 9 , wherein:
the filtering of high-frequency data includes using a low-pass filter including one or more parameters configured to devalue sensor data corresponding at least in part to sensor noise;
the filtering of low-frequency data includes using a high-pass filter including one or more parameters configured to devalue sensor data corresponding at least in part to sensor bias; and
the low-pass filter and the high-pass filter comprise one or more of:
an exponential filter,
a finite impulse response filter,
an infinite impulse response filter,
an exponential decay filter, or
a median filter.

13. The one or more non-transitory computer-readable media of claim 9 , wherein the discrepancy metric includes a first residual that is associated with the first sensor and that is higher than a second residual associated with the second sensor.

14. The one or more non-transitory computer-readable media of claim 9 , wherein:
the first sensor and the second sensor include a first inertial measurement unit and a second IMU associated with respective positions relative to the vehicle;
the operations further comprise determining, by transformation to a reference point associated with the vehicle, transformed sensor data based on the first sensor data and the second sensor data; and
the discrepancy metric is based on the transformed sensor data.

15. The one or more non-transitory computer-readable media of claim 9 , wherein:
the first sensor data and the second sensor data include time series data; and
the operations further comprise beginning to filter the high-frequency data at an earlier time in the time series data than a later time at which beginning to filter the low-frequency data.

16. An autonomous vehicle comprising:
a first sensor and a second sensor;
one or more processors; and
one or more computer-readable media storing instructions that, when executed by the one or more processors, cause the autonomous vehicle to perform operations comprising:
receiving first sensor data associated with the first sensor and second sensor data associated with the second sensor;
determining third sensor data by filtering high-frequency data and low-frequency data from the first sensor data and the second sensor data;
determining, based at least in part on the third sensor data, a discrepancy metric associated with the first sensor;
determining, based at least in part on the discrepancy metric exceeding a threshold metric, an error associated with the first sensor; and
using, based at least in part on the error associated with the first sensor, the second sensor data instead of the first sensor data to control of the autonomous vehicle.

17. The autonomous vehicle of claim 16 , wherein:
the filtering of high-frequency data

Assignees

• Zoox Inc

Inventors

• Adams Derek
• Senant Eric Andre
• Ko Weihung
• Bosse Michael Carsten

Classifications

• B60W50/0098 Primary

  Details of control systems ensuring comfort, safety or stability not otherwise provided for · CPC title

• B60W60/001

  Planning or execution of driving tasks · CPC title

• G01C21/165

  combined with non-inertial navigation instruments · CPC title

• G01C21/183

  Compensation of inertial measurements, e.g. for temperature effects · CPC title

• B60W2050/0055

  High-pass filters · CPC title