# SLICT: Multi-input Multi-scale Surfel-Based Lidar-Inertial Continuous-Time Odometry and Mapping

arXiv: https://arxiv.org/abs/2211.03900 · HTML: https://mlsanity.com/paper/2211.03900/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2022.

## Built on by (most cited first, 11 of 11)

- [MCD: Diverse Large-Scale Multi-Campus Dataset for Robot Perception](https://mlsanity.com/paper/2403.11496/) (2024)
- [Incremental Joint Learning of Depth, Pose and Implicit Scene Representation on Monocular Camera in Large-scale Scenes](https://mlsanity.com/paper/2404.06050/) (2024)
- [HiMo: High-Speed Objects Motion Compensation in Point Clouds](https://mlsanity.com/paper/2503.00803/) (2025)
- [A Dynamic Points Removal Benchmark in Point Cloud Maps](https://mlsanity.com/paper/2307.07260/) (2023)
- [BeautyMap: Binary-Encoded Adaptable Ground Matrix for Dynamic Points Removal in Global Maps](https://mlsanity.com/paper/2405.07283/) (2024)
- [Traj-LO: In Defense of LiDAR-Only Odometry Using an Effective Continuous-Time Trajectory](https://mlsanity.com/paper/2309.13842/) (2023)
- [MineInsight: A Multi-sensor Dataset for Humanitarian Demining Robotics in Off-Road Environments](https://mlsanity.com/paper/2506.04842/) (2025)
- [Traj-LIO: A Resilient Multi-LiDAR Multi-IMU State Estimator Through Sparse Gaussian Process](https://mlsanity.com/paper/2402.09189/) (2024)
- [PSS-BA: LiDAR Bundle Adjustment with Progressive Spatial Smoothing](https://mlsanity.com/paper/2403.06124/) (2024)
- [ULOC: Learning to Localize in Complex Large-Scale Environments with Ultra-Wideband Ranges](https://mlsanity.com/paper/2409.11122/) (2024)
- [SGBA: Semantic Gaussian Mixture Model-Based LiDAR Bundle Adjustment](https://mlsanity.com/paper/2410.01618/) (2024)

## Abstract (arXiv)

While feature association to a global map has significant benefits, to keep the computations from growing exponentially, most lidar-based odometry and mapping methods opt to associate features with local maps at one voxel scale. Taking advantage of the fact that surfels (surface elements) at different voxel scales can be organized in a tree-like structure, we propose an octree-based global map of multi-scale surfels that can be updated incrementally. This alleviates the need for recalculating, for example, a k-d tree of the whole map repeatedly. The system can also take input from a single or a number of sensors, reinforcing the robustness in degenerate cases. We also propose a point-to-surfel (PTS) association scheme, continuous-time optimization on PTS and IMU preintegration factors, along with loop closure and bundle adjustment, making a complete framework for Lidar-Inertial continuous-time odometry and mapping. Experiments on public and in-house datasets demonstrate the advantages of our system compared to other state-of-the-art methods. To benefit the community, we release the source code and dataset at https://github.com/brytsknguyen/slict.