Dynamic-LIVO: A Dynamic-Aware LiDAR-Inertial-Visual Odometry System Using Spatio-Temporal Normals

arXiv:2609.19336 · cs.RO · Submitted 2026-09-16 · Read on arXiv

cs.RO

Submitted: 2026-09-16

Updated: 2026-09-16

Code: https://github.com/MichaelGrupp/evo

Terminology

• Accuracy — 13× in this paper · explained in 1 episode
• LiDAR-Inertial-Visual Odometry — 6× in this paper
• Consistency — 5× in this paper · explained in 3 episodes
• Feature Extraction — 2× in this paper · explained in 2 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• C OMPARISON OF ABSOLUTE TRAJECTORY ERROR — 1× in this paper
• Calibration — 1× in this paper · explained in 7 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Residual Non-static Rate — 1× in this paper
• Static Removal Rate — 1× in this paper
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Dynamic Object Detection in Range data using Spatiotemporal Normals

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