Lidar localization using optical flow
A method for determining a lidar sensor pose with respect to a predefined map image, comprising acquiring a lidar height map; determining an optical flow field, which relates the lidar height map and the map image; and computing a maximum-likelihood/ML estimate of the lidar sensor pose on the basis of the determined optical flow field. The optical flow field may optionally be determined by a regression model, which additionally produces an associated variability tensor to be used in the ML estimation. In particular, the optical flow field may be determined by a trained neural network.
1. A method for determining a lidar sensor pose with respect to a predefined map image, comprising:
acquiring, by a computing device, a lidar height map;
determining, by a regression model implemented by the computing device, an optical flow field, which relates the lidar height map and the map image, wherein the optical flow field associates image points in the map image or points in the lidar height map with local translation vectors;
further determining, by the regression model, a variability tensor associated with the optical flow field; and
computing, by the computing device, a maximum-likelihood (ML) estimate of the lidar sensor pose on the basis of the determined optical flow field and the variability tensor.
2. The method of claim 1 , wherein the computing of the ML estimate of the lidar sensor pose includes maximizing a likelihood of a candidate error correction transform given the determined optical flow field.
3. The method of claim 1 , further comprising:
augmenting the lidar height map with lidar intensity information,
wherein the optical flow field is determined on the basis of the augmented height map and the map image.
4. The method of claim 1 , wherein the optical flow field is a two-dimensional optical flow.
5. The method of claim 1 , wherein the optical flow field is a three-dimensional generalized optical flow.
6. The method of claim 1 , further comprising:
pre-processing the lidar height map and the map image into respective feature images; and
sparsening the feature image of the map image,
wherein the optical flow field is detected on the basis of the respective features images.
7. The method of claim 1 , further comprising:
initially obtaining a coarse global localization; and
extracting the map image as a subarea of a larger predetermined map image.
8. The method of claim 1 , wherein the regression model is implemented by a trained neural network.
9. The method of claim 1 , further comprising repeating the steps of determining an optical flow field and computing an ML estimate of the lidar sensor pose, together with optional further steps, at an increased spatial resolution and applying the estimated lidar sensor pose as a prior.
10. A navigation system comprising:
a communication interface for acquiring a lidar height map;
a memory adapted for storing a predefined map image;
first processing circuitry, which implements a regression model configured to determine an optical flow field, which relates the lidar height map and the map image, wherein the optical flow field associates image points in the map image or points in the lidar height map with local translation vectors;
wherein the regression model is further configured to determine a variability tensor associated with the optical flow field; and
second processing circuitry configured to compute a maximum-likelihood (ML) estimate of a lidar sensor pose on the basis of the determined optical flow field and the variability tensor.
11. The navigation system of claim 10 , wherein the first processing circuitry includes a trainable component.
12. The navigation system of claim 10 , further comprising a Kalman filter configured for position tracking at least partly on the basis of the estimated lidar pose.
13. A computer program product stored on a non-transitory computer-readable storage medium and including instructions to cause a processor device to:
acquire a lidar height map;
determine, by a regression model implemented by the processor device, an optical flow field, which relates the lidar height map and a map image, wherein the optical flow field associates image points in the map image or points in the lidar height map with local translation vectors;
determine, by the regression model, a variability tensor associated with the optical flow field; and
compute a maximum-likelihood (ML) estimate of a lidar sensor pose on the basis of the determined optical flow field and the variability tensor.