MI-NDT: Multiscale Iterative Normal Distribution Transform for Registering Large-Scale Outdoor Scans

摘要

Point cloud registration is crucial for various applications such as robotics and urban planning, yet it remains challenging due to issues such as noise, variable resolutions, and uncertainties in the initial pose. The normal distribution transform (NDT) algorithm has become a standard approach for registering point scans, but its application to large-scale scenes faces significant challenges due to inherent limitations. These include computational complexity, susceptibility to noise interference, limited adaptability to varying voxel resolutions, and substantial initial pose deviations. To address these issues, we propose the multiscale iterative NDT (MI-NDT) through a meticulously designed multiscale iterative optimization framework. The MI-NDT algorithm voxelizes the reference scan into multiscale voxels based on the optimal flatness ratio criterion. At each scale, the classic NDT algorithm computes the pose of the source scan. The pose obtained from coarse voxels serves as input for the next optimization step on finer voxels. This iterative refinement continues until the finest voxel level, where the resulting pose corresponds to the optimal translation parameters. The experimental results demonstrate the effectiveness of the MI-NDT algorithm in terms of registration accuracy and robustness. Comparative experiments with existing algorithms show significant improvements in accuracy, especially under varying voxel resolutions and noise levels. Moreover, the algorithm exhibits enhanced tolerance to initial pose deviations, enabling successful registration even with poor initial poses.