Loosely coupled 4D-Radar-Inertial Odometry for Ground Robots

TL;DR

This paper introduces a robust graph-based radar-inertial odometry method for ground robots, improving accuracy and computational efficiency in challenging environments by enhancing ego-velocity estimation and maintaining fixed computational costs.

Contribution

The paper presents a novel graph-based optimization framework for radar-inertial odometry, with an improved ego-velocity estimation for ground vehicles, outperforming existing methods on the NTU4DRadLM dataset.

Findings

Outperforms existing algorithms on most trajectories in the NTU4DRadLM dataset.

Maintains fixed computational costs with a sliding window approach.

Enhances ego-velocity estimation for ground vehicles, improving odometry accuracy.

Abstract

Accurate robot odometry is essential for autonomous navigation. While numerous techniques have been developed based on various sensor suites, odometry estimation using only radar and IMU remains an underexplored area. Radar proves particularly valuable in environments where traditional sensors, like cameras or LiDAR, may struggle, especially in low-light conditions or when faced with environmental challenges like fog, rain or smoke. However, despite its robustness, radar data is noisier and more prone to outliers, requiring specialized processing approaches. In this paper, we propose a graph-based optimization approach using a sliding window for radar-based odometry, designed to maintain robust relationships between poses by forming a network of connections, while keeping computational costs fixed (specially beneficial in long trajectories). Additionally, we introduce an enhancement in the ego-velocity estimation specifically for ground vehicles, both holonomic and non-holonomic, which subsequently improves the direct odometry input required by the optimizer. Finally, we present a comparative study of our approach against existing algorithms, showing how our pure odometry approach inproves the state of art in most trajectories of the NTU4DRadLM dataset, achieving promising results when evaluating key performance metrics.