Vehicle localisation

Patent metadata

Field	Value
Publication number	US-12085403-B2
Application number	US-202117562478-A
Country	US
Kind code	B2
Filing date	Dec 27, 2021
Priority date	Dec 28, 2020
Publication date	Sep 10, 2024
Grant date	Sep 10, 2024

Abstract

The present disclosure relates to a method for determining a vehicle pose, predicting a pose (x k , y k , θ k ) of vehicle on a map based on sensor data acquired by a vehicle localization system, transforming a set of map road references of a segment of a digital map from a global coordinate system to an image-frame coordinate system of a vehicle-mounted camera based on map data and predicted pose of the vehicle. The transformed set of map road references form a set of polylines in image-frame coordinate system. Identifying a set of corresponding image road reference features in an image acquired by vehicle mounted camera, where each identified road references feature defines a set of measurement coordinates (x i , y i ) in image-frame. Projecting each of identified set of image road reference features onto formed set of polylines in order to obtain a set of projection points.

First claim

What is claimed is:

1. A method for determining a vehicle pose, the method comprising:
predicting a pose of the vehicle on a map based on sensor data acquired by a vehicle localization system;
transforming a set of map road references of a segment of a digital map from a global coordinate system to an image-frame coordinate system of a vehicle-mounted camera based on map data and the predicted pose of the vehicle, wherein the transformed set of map road references form a set of polylines in the image-frame coordinate system;
identifying a set of corresponding image road reference features in an image acquired by the vehicle-mounted camera, each identified road references feature defining a set of measurement coordinates in the image-frame;
projecting each of the identified set of image road reference features onto the formed set of polylines in order to obtain a set of projection points, wherein each projection point defines a set of projection coordinates;
determining an error parameter based on a difference between the measurement coordinates and the corresponding projection coordinates;
updating the predicted pose based on the determined error parameter; and
controlling the vehicle based on the updated pose.

2. The method according to claim 1 , wherein the step of transforming the set of map road reference comprises:
converting the set of map road references of the segment of the digital map from the global coordinate system into an ego-frame coordinate system of the vehicle based on map data and the predicted pose; and
transforming the converted set of map road references of the segment from the ego-frame coordinate system to the image-frame coordinate system based on a set of calibration parameters of the vehicle-mounted camera.

3. The method according to claim 2 , wherein the calibration parameters include a set of camera extrinsic parameters and a set of camera intrinsic parameters.

4. The method according to claim 1 , wherein the step of predicting a pose of the vehicle comprises predicting a pose of the vehicle on a map based on sensor data acquired by the vehicle localization system and a predefined vehicle motion model.

5. The method according to claim 1 , wherein the step of projecting the identified set of image road reference features comprises:
for each identified image road reference feature, defining a closest index of each polyline relative to the image road reference feature as the projection point for that image road reference feature.

6. The method according to claim 5 , further comprising:
validating the identified set of image road reference features by:
for each image road reference feature, discarding the image road reference features and the associated projection points if one of the associated projection points is a non-orthogonal projection point;
wherein the determination of the error parameter is only based on a difference between the measurement coordinates of validated road reference features and the corresponding projection coordinates.

7. The method according to claim 1 , wherein the step of predicting the pose of the vehicle comprises:
predicting the pose of the vehicle using a Bayesian filter.

8. The method according to claim 1 , wherein the step of predicting the pose of the vehicle comprises perturbing an estimated current vehicle pose and propagating the perturbed vehicle pose; and
wherein the transformation of the set of map road references are based on the perturbed vehicle poses.

9. The method according to claim 8 , wherein the perturbing an estimated current vehicle pose and propagating the perturbed vehicle poses is based on prediction and measurement models of a Cubature Kalman Filter, and wherein the perturbed vehicle poses correspond to cubature points.

10. The method according to claim 1 , further comprising:
selecting the segment of the digital map based on the predicted pose of the vehicle and a set of properties of the vehicle-mounted camera.

11. A non-transitory computer-readable storage medium storing one or more instructions configured to be executed by one or more processors of a vehicle localization module, the one or more instructions for performing the method according to claim 1 .

12. A device for determining a vehicle pose, the device comprising control circuitry configured to:
predict a pose of the vehicle on a map based on sensor data acquired by a vehicle localization system;
transform a set of map road references of a segment of a digital map from a global coordinate system to an image-frame coordinate system of a vehicle-mounted camera based on map data and the predicted pose of the vehicle, wherein the transformed set of map road references form a set of polylines in the image-frame coordinate system;
identify a set of corresponding image road reference features in an image acquired by the vehicle-mounted camera, each identified image road references feature defining a set of measurement coordinates in the image-frame;
project each of the set of identified road reference features onto the formed set of polylines in order to obtain a set of projection points, wherein each projection point defines a set of projection coordinates;
determine an error parameter based on a difference between the measurement coordinates and the corresponding projection coordinates;
update the predicted pose based on the determined error parameter; and
control the vehicle based on the updated pose.

13. A vehicle comprising:
a localization system for monitoring a position of the vehicle;
a vehicle-mounted camera for capturing images of a surrounding environment of the vehicle;
a device for determining a vehicle pose, the device comprising control circuitry configured to:
predict a pose of the vehicle on a map based on sensor data acquired by a vehicle localization system;
transform a set of map road references of a segment of a digital map from a global coordinate system to an image-frame coordinate system of a vehicle-mounted camera based on map data and the predicted pose of the vehicle, wherein the transformed set of map road references form a set of polylines in the image-frame coordinate system;
identify a set of corresponding image road reference features in an image acquired by the vehicle-mounted camera, each identified image road references feature defining a set of measurement coordinates in the image-frame;
project each of the set of identified road reference features onto the formed set of polylines in order to obtain a set of projection points, wherein each projection point defines a set of projection coordinates;
determine an error parameter based on a difference between the measurement coordinates and the corresponding projection coordinates
update the predicted pose based on the determined error parameter; and
control the vehicle based on the updated pose.

Assignees

• Zenseact Ab

Inventors

• Fu Junsheng
• Zhang Han
• Gustafsson Tony
• Schindler Andreas
• Sesma Caselles Eduardo
• Steinmetz Erik
• Kielén Pontus
• Beauvisage Axel
• Lin Sörstedt Joakim

Classifications

• B60W2420/403

  Image sensing, e.g. optical camera · CPC title

• G01C21/3694

  Output thereof on a road map · CPC title

• G01C21/3644

  Landmark guidance, e.g. using POIs or conspicuous other objects · CPC title

• B60W40/10

  related to vehicle motion · CPC title

• G06V20/588

  Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road · CPC title