Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights

TL;DR

This paper introduces a deep learning method to enhance radar-lidar localization by learning to weight radar points, improving accuracy and robustness in autonomous driving scenarios.

Contribution

It proposes a novel learned weighting scheme for radar points in ICP-based localization, supported by a new differentiable ICP library for training.

Findings

Improved localization accuracy over traditional methods

Enhanced ICP convergence with learned weights

Effective filtering of artefacts and noise in radar data

Abstract

This paper presents a novel deep-learning-based approach to improve localizing radar measurements against lidar maps. This radar-lidar localization leverages the benefits of both sensors; radar is resilient against adverse weather, while lidar produces high-quality maps in clear conditions. However, owing in part to the unique artefacts present in radar measurements, radar-lidar localization has struggled to achieve comparable performance to lidar-lidar systems, preventing it from being viable for autonomous driving. This work builds on ICP-based radar-lidar localization by including a learned preprocessing step that weights radar points based on high-level scan information. To train the weight-generating network, we present a novel, stand-alone, open-source differentiable ICP library. The learned weights facilitate ICP by filtering out harmful radar points related to artefacts, noise, and even vehicles on the road. Combining an analytical approach with a learned weight reduces overall localization errors and improves convergence in radar-lidar ICP results run on real-world autonomous driving data. Our code base is publicly available to facilitate reproducibility and extensions.