# Learning a Bias Correction for Lidar-only Motion Estimation

arXiv: https://arxiv.org/abs/1801.04678 · HTML: https://mlsanity.com/paper/1801.04678/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2018.

## Built on by (most cited first, 3 of 3)

- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [Registration of 3D Point Sets Using Correntropy Similarity Matrix](https://mlsanity.com/paper/2107.09725/) (2021)
- [DNN Filter for Bias Reduction in Distribution-to-Distribution Scan Matching](https://mlsanity.com/paper/2211.04047/) (2022)

## Abstract (arXiv)

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.