3 research outputs found
AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments
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.Comment: 18 pages, 18 figur
ALITA: A Large-scale Incremental Dataset for Long-term Autonomy
For long-term autonomy, most place recognition methods are mainly evaluated
on simplified scenarios or simulated datasets, which cannot provide solid
evidence to evaluate the readiness for current Simultaneous Localization and
Mapping (SLAM). In this paper, we present a long-term place recognition dataset
for use in mobile localization under large-scale dynamic environments. This
dataset includes a campus-scale track and a city-scale track: 1) the
campus-track focuses the long-term property, we record LiDAR device and an
omnidirectional camera on 10 trajectories, and each trajectory are repeatly
recorded 8 times under variant illumination conditions. 2) the city-track
focuses the large-scale property, we mount the LiDAR device on the vehicle and
traversing through a 120km trajectories, which contains open streets,
residential areas, natural terrains, etc. They includes 200 hours of raw data
of all kinds scenarios within urban environments. The ground truth position for
both tracks are provided on each trajectory, which is obtained from the Global
Position System with an additional General ICP based point cloud refinement. To
simplify the evaluation procedure, we also provide the Python-API with a set of
place recognition metrics is proposed to quickly load our dataset and evaluate
the recognition performance against different methods. This dataset targets at
finding methods with high place recognition accuracy and robustness, and
providing real robotic system with long-term autonomy. The dataset and the
provided tools can be accessed from https://github.com/MetaSLAM/ALITA.Comment: 4 pages, 2 figure