Dynamic calibration of lidar sensors

1. A method of calibrating a LiDAR sensor mounted on a vehicle, the method comprising:

storing a reference three-dimensional image acquired by the LiDAR sensor while the LiDAR sensor is in an expected alignment with respect to the vehicle, the reference three-dimensional image including a first image of a fixed feature on the vehicle, wherein:

the LiDAR sensor is positioned behind a windshield of the vehicle; and

the fixed feature comprises a mask attached to an area of the windshield that is directly in front of the LiDAR sensor, wherein the mask is configured to block light in an operating wavelength of the LiDAR sensor and is shaped to block a portion of a field of view of the LiDAR sensor;

acquiring, using the LiDAR sensor, a three-dimensional image including a second image of the fixed feature; and

determining a deviation from the expected alignment of the LiDAR sensor with respect to the vehicle by comparing the second image of the fixed feature in the three-dimensional image to the first image of the fixed feature in the reference three-dimensional image.

2. The method of claim 1 , further comprising:

re-calibrating the LiDAR sensor based on the deviation from the expected alignment of the LiDAR sensor with respect to the vehicle.

3. The method of claim 1 , further comprising:

determining a transformation to be applied to the second image of the fixed feature in the three-dimensional image so as to match the first image of the fixed feature in the reference three-dimensional image; and

re-calibrating the LiDAR sensor based on the transformation.

4. The method of claim 1 , further comprising:

determining that the deviation from the expected alignment of the LiDAR sensor exceeds a threshold; and

providing an alert in response to determining that the deviation from the expected alignment of the LiDAR sensor exceeds the threshold.

5. The method of claim 1 , wherein the reference three-dimensional image is acquired by the LiDAR sensor after the LiDAR sensor has been pre-calibrated in a manufacturing facility.

6. The method of claim 1 , wherein the deviation from the expected alignment of the LiDAR sensor includes one or more of a yaw deviation, a roll deviation, a pitch deviation, and translational deviations along three orthogonal axes.

7. The method of claim 1 , wherein the fixed feature comprises a portion of a hood of the vehicle or an object attached to the hood.

8. The method of claim 1 , wherein the mask has an outer boundary and an inner boundary, the inner boundary being sized so that the mask encroaches a perimeter of the field of view of the LiDAR sensor.

9. The method of claim 1 , further comprising:

acquiring, using the LiDAR sensor while the vehicle is traveling on a road with fixed road features, one or more three-dimensional images, each of the one or more three-dimensional images including images of the fixed road features;

analyzing a spatial relationship between the images of the fixed road features in the one or more three-dimensional images and an orientation of a field of view of the LiDAR sensor; and

determining the deviation from the expected alignment of the LiDAR sensor with respect to the vehicle based on the spatial relationship between the images of the fixed road features and the field of view of the LiDAR sensor.

10. The method of claim 9 , wherein:

the road fixed features comprise one or more pairs of lane markings on either side of the vehicle;

analyzing the spatial relationship comprises determining a pitch angle between one pair of lane markings of the one or more pairs of lane markings and the field of view of the LiDAR sensor; and

determining the deviation from the expected alignment of the LiDAR sensor comprises determining a pitch error of the LiDAR sensor based on the pitch angle.

11. A method of calibrating a LiDAR sensor mounted on a vehicle, the method comprising:

acquiring, using the LiDAR sensor while the vehicle is traveling on a road with fixed road features, one or more three-dimensional images, each of the one or more three-dimensional images including images of the fixed road features;

analyzing a spatial relationship between the images of the fixed road features in the one or more three-dimensional images and an orientation of a field of view of the LiDAR sensor; and

determining a deviation from an expected alignment of the LiDAR sensor with respect to the vehicle based on the spatial relationship between the images of the fixed road features and the field of view of the LiDAR sensor.

12. The method of claim 11 , further comprising:

re-calibrating the LiDAR sensor based on the deviation from the expected alignment of the LiDAR sensor with respect to the vehicle.

13. The method of claim 11 , further comprising:

determining that the deviation from the expected alignment of the LiDAR sensor exceeds a threshold; and

providing an alert in response to determining that the deviation from the expected alignment of the LiDAR sensor exceeds the threshold.

14. The method of claim 11 , wherein:

the fixed road features comprise one or more pairs of lane markings on either side of the vehicle;

analyzing the spatial relationship comprises determining a pitch angle between one pair of lane markings of the one or more pairs of lane markings and the field of view of the LiDAR sensor; and

determining the deviation from the expected alignment of the LiDAR sensor comprises determining a pitch error of the LiDAR sensor based on the pitch angle.

15. The method of claim 11 , wherein:

the fixed road features comprise one or more pairs of lane markings on either side of the vehicle;

the one or more pairs of lane markings include a first lane marking on a driver side of the vehicle and a second lane marking on a passenger side of the vehicle;

analyzing the spatial relationship comprises determining a height difference between the first lane marking and the second lane marking; and

determining the deviation from the expected alignment of the LiDAR sensor comprises determining a roll error of the LiDAR sensor based on the height difference.

16. The method of claim 11 , wherein:

the fixed road features comprise one or more pairs of lane markings on either side of the vehicle; and

the one or more three-dimensional images comprise a plurality of three-dimensional images acquired by the LiDAR sensor over an interval of time as the vehicle is traveling on the road for an interval of distance.

17. The method of claim 16 , wherein the road is substantially straight and level over the interval of distance.

18. The method of claim 16 , further comprising:

determining a path of the vehicle over the interval of distance; and

comparing the path of the vehicle to paths of the fixed road features from the plurality of three-dimensional images.

19. The method of claim 18 wherein:

the one or more pairs of lane markings include a first lane marking on a driver side of the vehicle and a second lane marking on a passenger side of the vehicle;

analyzing the spatial relationship comprises determining an amount of lateral asymmetry between the first lane marking from the path of the vehicle and the second lane marking from the path of the vehicle; and

determining the deviation from the expected alignment of the LiDAR sensor comprises determining a yaw error of the LiDAR sensor based on the amount of lateral asymmetry.

20. The method of claim 18 , wherein:

analyzing the spatial relationship comprises determining a height difference between one pair of lane markings and the path of the vehicle; and

determining the deviation from the expected alignment of the LiDAR sensor comprises determining a vertical error of the LiDAR sensor based on the height difference.