PCR-Pro: 3D Sparse and Different Scale Point Clouds Registration and Robust Estimation of Information Matrix For Pose Graph SLAM
This work addresses a specific challenge in robotics and computer vision for SLAM applications, offering an incremental improvement over existing methods.
The paper tackles the problem of registering 3D point clouds with different scales and sparsity, which current ICP methods cannot handle, by proposing an efficient algorithm that detects scale differences and computes transformations, resulting in improved covariance matrix estimation for pose-graph SLAM.
For both indoor and outdoor environments, we propose an efficient and novel method for different scales and sparse 3D point clouds registration that cannot be handled by the current popular ICP approaches. Our algorithm efficiently detects the scale difference between point clouds and uses the keyframes to estimate the relative pose for calculating the scale difference. The algorithm applies a filter and computes the final transformation which coverages to a global minimum. The good estimation of transform and scale helps in the calculation of the covariance matrix using a closed form solution efficiently. This covariance between point clouds helps in the estimation of information matrix for pose-graph SLAM.