# Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments

arXiv: https://arxiv.org/abs/2104.14938 · HTML: https://mlsanity.com/paper/2104.14938/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2021.

## Built on by (most cited first, 26 of 26)

- [Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis](https://mlsanity.com/paper/2312.08782/) (2023)
- [How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability](https://mlsanity.com/paper/2209.10788/) (2022)
- [MAC-VO: Metrics-aware Covariance for Learning-based Stereo Visual Odometry](https://mlsanity.com/paper/2409.09479/) (2024)
- [Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for Off-Road Navigation](https://mlsanity.com/paper/2302.00134/) (2023)
- [SiLVR: Scalable Lidar-Visual Reconstruction with Neural Radiance Fields for Robotic Inspection](https://mlsanity.com/paper/2403.06877/) (2024)
- [UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubT](https://mlsanity.com/paper/2206.08185/) (2022)
- [AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability](https://mlsanity.com/paper/2501.15659/) (2025)
- [AirIMU: Learning Uncertainty Propagation for Inertial Odometry](https://mlsanity.com/paper/2310.04874/) (2023)
- [Unified Representation of Geometric Primitives for Graph-SLAM Optimization Using Decomposed Quadrics](https://mlsanity.com/paper/2108.08957/) (2021)
- [SceneBot: Contact-Prompted General Humanoid Whole Body Tracking with Scene-Interaction](https://mlsanity.com/paper/2606.27581/) (2026)
- [Deep Bayesian Future Fusion for Self-Supervised, High-Resolution, Off-Road Mapping](https://mlsanity.com/paper/2403.11876/) (2024)
- [SiLVR: Scalable Lidar-Visual Radiance Field Reconstruction with Uncertainty Quantification](https://mlsanity.com/paper/2502.02657/) (2025)
- [Can Vision Foundation Models Navigate? Zero-Shot Real-World Evaluation and Lessons Learned](https://mlsanity.com/paper/2603.25937/) (2026)
- [Spatiotemporal Contrastive Learning for Cross-View Video Localization in Unstructured Off-road Terrains](https://mlsanity.com/paper/2506.05250/) (2025)
- [Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration](https://mlsanity.com/paper/2304.14520/) (2023)
- [Enhancing Scene Coordinate Regression with Efficient Keypoint Detection and Sequential Information](https://mlsanity.com/paper/2412.06488/) (2024)
- [Map Prediction and Generative Entropy for Multi-Agent Exploration](https://mlsanity.com/paper/2501.13189/) (2025)
- [Holistic Fusion: Task- and Setup-Agnostic Robot Localization and State Estimation with Factor Graphs](https://mlsanity.com/paper/2504.06479/) (2025)
- [JVLDLoc: a Joint Optimization of Visual-LiDAR Constraints and Direction Priors for Localization in Driving Scenario](https://mlsanity.com/paper/2208.09777/) (2022)
- [Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems](https://mlsanity.com/paper/2308.07264/) (2023)
- [Learning-based GNSS Uncertainty Quantification using Continuous-Time Factor Graph Optimization](https://mlsanity.com/paper/2503.04933/) (2025)
- [CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environments](https://mlsanity.com/paper/2505.06483/) (2025)
- [CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories](https://mlsanity.com/paper/2507.09624/) (2025)
- [BEV-Patch-PF: Particle Filtering with BEV-Aerial Feature Matching for Off-Road Geo-Localization](https://mlsanity.com/paper/2512.15111/) (2025)
- [VISTA: Scale-Aware Visual Navigation via Action History Conditioning](https://mlsanity.com/paper/2606.17294/) (2026)
- [Adapting Generalist Vehicle Models for High-Speed MPC Across Terrains](https://mlsanity.com/paper/2607.13319/) (2026)

## Abstract (arXiv)

We propose Super Odometry, a high-precision multi-modal sensor fusion framework, providing a simple but effective way to fuse multiple sensors such as LiDAR, camera, and IMU sensors and achieve robust state estimation in perceptually-degraded environments. Different from traditional sensor-fusion methods, Super Odometry employs an IMU-centric data processing pipeline, which combines the advantages of loosely coupled methods with tightly coupled methods and recovers motion in a coarse-to-fine manner. The proposed framework is composed of three parts: IMU odometry, visual-inertial odometry, and laser-inertial odometry. The visual-inertial odometry and laser-inertial odometry provide the pose prior to constrain the IMU bias and receive the motion prediction from IMU odometry. To ensure high performance in real-time, we apply a dynamic octree that only consumes 10 % of the running time compared with a static KD-tree. The proposed system was deployed on drones and ground robots, as part of Team Explorer's effort to the DARPA Subterranean Challenge where the team won 1st and 2nd place in the Tunnel and Urban Circuits, respectively.