SLICT: Multi-Input Multi-Scale Surfel-Based Lidar-Inertial Continuous-Time Odometry and Mapping

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.