Dr-PoGO: Direct Radar Pose-Graph Optimization

TL;DR

Dr-PoGO is a radar-based SLAM method that uses direct registration and pose-graph optimization for robust localization in adverse weather, with publicly available code.

Contribution

It introduces a novel direct registration approach for radar SLAM that integrates place recognition and pose-graph optimization, improving robustness and accuracy.

Findings

State-of-the-art performance over 300km of data in automotive environments.

Robust SLAM in adverse weather conditions using radar data.

Effective coarse-to-fine registration combining visual features and descriptors.

Abstract

This paper introduces Dr-PoGO, a method for Simultaneous Localization And Mapping (SLAM) using a 2D spinning radar. Unlike cameras or lidars that require line-of-sight, millimetre-wave radars can `see' through dust, falling snow, rain, etc. Accordingly, it is a great modality for robust perception regardless of the weather conditions. While most existing radar-based SLAM methods rely on the extraction of point clouds or features to perform ego-motion estimation, Dr-PoGO leverages direct registration techniques for odometry (DRO) and loop-closure registration. An off-the-shelf radar-focused place recognition algorithm, RaPlace, provides loop-closure candidates. As RaPlace does not provide relative transformations, Dr-PoGO introduces a coarse-to-fine registration that uses visual features and descriptors to obtain an initial guess for the direct transformation refinement. The global trajectory is optimized in a pose-graph optimization. Dr-PoGO demonstrates state-of-the-art performance over 300km of data in various real-world automotive environments. Our implementation is publicly available: https://github.com/utiasASRL/dr_pogo.