LoCal-RIO: Radar-Inertial Odometry with Loop-Closure IMU Bias Calibration

arXiv:2603.14109 · cs.RO · Submitted 2026-03-14 · Read on arXiv

cs.RO

Submitted: 2026-03-14

Updated: 2026-09-18

Code: https://github.com/borglab/gtsam

Terminology

• Alignment — 7× in this paper · explained in 1 episode
• Accuracy — 6× in this paper · explained in 1 episode
• Ablation — 4× in this paper · explained in 1 episode
• Calibration — 4× in this paper · explained in 7 episodes
• LiDAR-Inertial Odometry — 4× in this paper
• Point Clouds — 3× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• FD-RIO: Fast Dense Radar Inertial Odometry
• Continuous-Time Radar-Inertial and Lidar-Inertial Odometry using a Gaussian Process Motion Prior
• Learning Point Correspondences In Radar 3D Point Clouds For Radar-Inertial Odometry
• Equi-RO: A 4D mmWave Radar Odometry via Equivariant Networks
• Impact of Temporal Delay on Radar-Inertial Odometry
• Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud
• FAST-LIO2: Fast Direct LiDAR-inertial Odometry

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving