Automatic Detection of Cross-Shaped Targets for Laser Scan Registration

Laser scan registration estimates a relative transformation to match one scan with another, based on the shape of the overlapping portions of the scans. The core and challenging problem of scan registration in a large-scale scene is, how to detect public targets among consecutive scans accurately and efficiently. We propose an automatic approach to detect cross-shaped targets (consisting of low-cost patterned paper) in raw LiDAR data. We exploit both radiometric and geometric information to fix cross-shaped targets, wherein a Morse function is formulated based on the discrete Morse theory on intensity image to detect the unique target pattern. Benefiting from both strategies of the imageand the geometry-based filtering, the target's center can be determined with sub-point precision. A detailed quantitative and qualitative comparison of the proposed algorithm with the state-of-the-art methods is presented. Results with real data from various complicated scenes, some of which have been successfully applied in entrusted projects, show that the proposed algorithm is faster and more reliable than the compared methods in target detection for serving 3-D registration.