Learning a Bias Correction for Lidar-only Motion Estimation

TL;DR

This paper introduces a learning-based bias correction method for lidar-only motion estimation, significantly improving accuracy with minimal additional computational cost.

Contribution

The paper proposes a novel Gaussian process regression approach to learn and correct biases in lidar odometry, enhancing existing methods' accuracy.

Findings

Achieved around 10% error reduction on multiple datasets.

Significant improvements in lidar odometry accuracy.

Less than 1% increase in computational cost.

Abstract

This paper presents a novel technique to correct for bias in a classical estimator using a learning approach. We apply a learned bias correction to a lidar-only motion estimation pipeline. Our technique trains a Gaussian process (GP) regression model using data with ground truth. The inputs to the model are high-level features derived from the geometry of the point-clouds, and the outputs are the predicted biases between poses computed by the estimator and the ground truth. The predicted biases are applied as a correction to the poses computed by the estimator. Our technique is evaluated on over 50km of lidar data, which includes the KITTI odometry benchmark and lidar datasets collected around the University of Toronto campus. After applying the learned bias correction, we obtained significant improvements to lidar odometry in all datasets tested. We achieved around 10% reduction in errors on all datasets from an already accurate lidar odometry algorithm, at the expense of only less than 1% increase in computational cost at run-time.