MAD-BA: 3D LiDAR Bundle Adjustment -- from Uncertainty Modelling to Structure Optimization
It addresses the need for joint pose and structure optimization in LiDAR-based state estimation for robotics, offering an open-source solution.
This paper introduces a framework for simultaneous optimization of sensor poses and 3D map (surfels) in LiDAR bundle adjustment, incorporating a generalized uncertainty model. It achieves improved performance over most comparable state-of-the-art methods on public datasets.
The joint optimization of sensor poses and 3D structure is fundamental for state estimation in robotics and related fields. Current LiDAR systems often prioritize pose optimization, with structure refinement either omitted or treated separately using implicit representations. This paper introduces a framework for simultaneous optimization of sensor poses and 3D map, represented as surfels. A generalized LiDAR uncertainty model is proposed to address less reliable measurements in varying scenarios. Experimental results on public datasets demonstrate improved performance over most comparable state-of-the-art methods. The system is provided as open-source software to support further research.