  We present AutoMerge, a LiDAR data processing framework for assembling a
large number of map segments into a complete map. Traditional large-scale map
merging methods are fragile to incorrect data associations, and are primarily
limited to working only offline. AutoMerge utilizes multi-perspective fusion
and adaptive loop closure detection for accurate data associations, and it uses
incremental merging to assemble large maps from individual trajectory segments
given in random order and with no initial estimations. Furthermore, after
assembling the segments, AutoMerge performs fine matching and pose-graph
optimization to globally smooth the merged map. We demonstrate AutoMerge on
both city-scale merging (120km) and campus-scale repeated merging (4.5km x 8).
The experiments show that AutoMerge (i) surpasses the second- and third- best
methods by 14% and 24% recall in segment retrieval, (ii) achieves comparable 3D
mapping accuracy for 120 km large-scale map assembly, (iii) and it is robust to
temporally-spaced revisits. To the best of our knowledge, AutoMerge is the
first mapping approach that can merge hundreds of kilometers of individual
segments without the aid of GPS.
