# Real-time Multi-Adaptive-Resolution-Surfel 6D LiDAR Odometry using Continuous-time Trajectory Optimization

arXiv: https://arxiv.org/abs/2105.02010 · HTML: https://mlsanity.com/paper/2105.02010/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 5 May 2021.

## Builds on (most cited first, 11 of 11)

- [The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truth](https://mlsanity.com/paper/2003.05691/) (2020)
- [LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices](https://mlsanity.com/paper/1912.05905/) (2019)
- [Tightly Coupled 3D Lidar Inertial Odometry and Mapping](https://mlsanity.com/paper/1904.06993/) (2019)
- [Efficient Derivative Computation for Cumulative B-Splines on Lie Groups](https://mlsanity.com/paper/1911.08860/) (2019)
- [Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping](https://mlsanity.com/paper/1810.06802/) (2018)
- [LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation](https://mlsanity.com/paper/1907.02233/) (2019)
- [UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes](https://mlsanity.com/paper/1912.09513/) (2019)
- [Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM](https://mlsanity.com/paper/1711.01691/) (2017)
- [Towards Resilient Autonomous Navigation of Drones](https://mlsanity.com/paper/2008.09679/) (2020)
- [Fast Autonomous Flight in Warehouses for Inventory Applications](https://mlsanity.com/paper/1809.06628/) (2018)
- [Autonomous Flight in Unknown GNSS-denied Environments for Disaster Examination](https://mlsanity.com/paper/2103.11742/) (2021)

## Built on by (most cited first, 9 of 9)

- [CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure](https://mlsanity.com/paper/2109.12979/) (2021)
- [DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2409.18092/) (2024)
- [Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles with Label Propagation for Cross-Domain Adaptation](https://mlsanity.com/paper/2210.09739/) (2022)
- [3D Semantic Scene Perception using Distributed Smart Edge Sensors](https://mlsanity.com/paper/2205.01460/) (2022)
- [Traj-LO: In Defense of LiDAR-Only Odometry Using an Effective Continuous-Time Trajectory](https://mlsanity.com/paper/2309.13842/) (2023)
- [Splat-LOAM: Gaussian Splatting LiDAR Odometry and Mapping](https://mlsanity.com/paper/2503.17491/) (2025)
- [Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles](https://mlsanity.com/paper/2108.06608/) (2021)
- [Online Localisation and Colored Mesh Reconstruction Architecture for 3D Visual Feedback in Robotic Exploration Missions](https://mlsanity.com/paper/2207.10489/) (2022)
- [LiDAR-based Registration against Georeferenced Models for Globally Consistent Allocentric Maps](https://mlsanity.com/paper/2412.02533/) (2024)

## Abstract (arXiv)

Simultaneous Localization and Mapping (SLAM) is an essential capability for autonomous robots, but due to high data rates of 3D LiDARs real-time SLAM is challenging. We propose a real-time method for 6D LiDAR odometry. Our approach combines a continuous-time B-Spline trajectory representation with a Gaussian Mixture Model (GMM) formulation to jointly align local multi-resolution surfel maps. Sparse voxel grids and permutohedral lattices ensure fast access to map surfels, and an adaptive resolution selection scheme effectively speeds up registration. A thorough experimental evaluation shows the performance of our approach on multiple datasets and during real-robot experiments.