Real-time 3-D feature detection and correspondence refinement for indoor environment-mapping using RGB-D cameras

The article presents an efficient method in detecting critical 3-D feature points for efficient and accurate data registration required in real-time indoor environment mapping by using RDB-D cameras. To achieve fast and accurate data correspondence between different 3-D scanned images, in the proposed method, RGB images are first used to detect two-dimensional (2-D) sparse color features for estimating matched pairs between successive scanned depth images. Critically, detected 2-D sparse features are mapped with their corresponding depth information. Consequently, sub-sets of matched pairs in 3-D depth space are established. Moreover, due to potential sensing noises, not all of pairs are valid and useful to 3-D matched pair establishment. Invalid pairs are detected and eliminated using an proposed angle-based filter for 2-D matched pairs, as well as a filter based on Euclidean distance, neighboring area and surface curvature filters for 3-D matched pairs. The experimental results show that the method is efficient and invariant to pose, robust for large-scale indoor environments, and feasible for real-time 3-D indoor environment mapping.