A Fast Histogram-Based Similarity Measure For Detecting Loop Closures In 3-D Lidar Data

We present a fast method of detecting loop closure opportunities through the use of similarity measures on histograms extracted from 3-D LIDAR data. We avoid computationally expensive features and compute histograms over simple global statistics of the LIDAR scans. The resulting histograms encode sufficient information to detect spatially close scans with high precision and recall and can be computed at rates faster than data acquisition on modest consumer-grade hardware. Our approach is able to match previously established results in LIDAR loop closure detection with less computational overhead.