RadarSplat-RIO：基于高斯点分布的室内雷达-惯性里程计
RadarSplat-RIO: Indoor Radar-Inertial Odometry with Gaussian Splatting-Based Radar Bundle Adjustment
AI总结：
本文提出基于高斯点分布的雷达束调整框架，通过联合优化雷达传感器姿态和场景几何，显著减少位姿漂移并提升鲁棒性，实验表明在多个室内场景中，雷达束调整性能优于传统雷达-惯性里程计。
摘要：
雷达比视觉和激光雷达在恶劣天气和光照条件下更具鲁棒性，但大多数雷达SLAM管线仍依赖帧间里程计，导致显著漂移。虽然闭环校正可以纠正长期误差，但需要重新访问地点并依赖强大的地点识别。相比之下，视觉里程计方法通常利用束调整（BA）在局部窗口内联合优化姿态和地图。然而，雷达的等效BA公式仍鲜有探索。我们提出了首个基于高斯点分布（GS）的雷达BA框架，利用全范围-方位-多普勒数据联合优化雷达传感器姿态和场景几何，首次将多帧BA的优势引入雷达。当与现有雷达-惯性里程计前端集成时，我们的方法显著减少了位姿漂移并提高了鲁棒性。在多个室内场景中，我们的雷达BA在多个指标上优于传统雷达-惯性里程计，平均绝对平移和旋转误差分别减少了90%和80%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
