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

TL;DR

SLICT introduces a multi-scale surfel-based global map structure for lidar-inertial odometry that improves computational efficiency and robustness through incremental updates, multi-sensor input, and a comprehensive optimization framework.

Contribution

The paper presents a novel octree-based multi-scale surfel map and a complete continuous-time lidar-inertial odometry system with advanced data association and optimization techniques.

Findings

Outperforms state-of-the-art methods on public datasets.

Efficient incremental map updates reduce computational load.

Robust multi-sensor input enhances performance in degenerate scenarios.

Abstract

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.