RadarSplat-RIO: Indoor Radar-Inertial Odometry with Gaussian Splatting-Based Radar Bundle Adjustment

TL;DR

This paper introduces a novel radar bundle adjustment framework using Gaussian Splatting that jointly optimizes poses and scene geometry, significantly reducing drift in indoor radar-inertial odometry.

Contribution

It is the first to formulate a radar bundle adjustment method leveraging Gaussian Splatting, enhancing accuracy and robustness over previous radar odometry techniques.

Findings

Reduces pose drift by 90% in translation and 80% in rotation.

Achieves substantial accuracy improvements over prior radar-inertial odometry.

Demonstrates effectiveness across multiple indoor scenes.

Abstract

Radar is more resilient to adverse weather and lighting conditions than visual and Lidar simultaneous localization and mapping (SLAM). However, most radar SLAM pipelines still rely heavily on frame-to-frame odometry, which leads to substantial drift. While loop closure can correct long-term errors, it requires revisiting places and relies on robust place recognition. In contrast, visual odometry methods typically leverage bundle adjustment (BA) to jointly optimize poses and map within a local window. However, an equivalent BA formulation for radar has remained largely unexplored. We present the first radar BA framework enabled by Gaussian Splatting (GS), a dense and differentiable scene representation. Our method jointly optimizes radar sensor poses and scene geometry using full range-azimuth-Doppler data, bringing the benefits of multi-frame BA to radar for the first time. When integrated with an existing radar-inertial odometry frontend, our approach significantly reduces pose drift and improves robustness. Across multiple indoor scenes, our radar BA achieves substantial gains over the prior radar-inertial odometry, reducing average absolute translational and rotational errors by 90% and 80%, respectively.