Efficient Continuous-time SLAM for 3D Lidar-based Online Mapping

Video spotlight for paper:
David Droeschel and Sven Behnke:
"Efficient Continuous-time SLAM for 3D Lidar-based Online Mapping", IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, May 2018.
http://www.ais.uni-bonn.de/papers/ICRA_2018_Droeschel.pdf

Abstract—Modern 3D laser-range scanners have a high data
rate, making online simultaneous localization and mapping
(SLAM) computationally challenging. Recursive state estimation
techniques are efficient but commit to a state estimate
immediately after a new scan is made, which may lead to
misalignments of measurements. We present a 3D SLAM
approach that allows for refining alignments during online
mapping. Our method is based on efficient local mapping and
a hierarchical optimization back-end. Measurements of a 3D
laser scanner are aggregated in local multiresolution maps by
means of surfel-based registration. The local maps are used in
a multi-level graph for allocentric mapping and localization. In
order to incorporate corrections when refining the alignment,
the individual 3D scans in the local map are modeled as a
sub-graph and graph optimization is performed to account for
drift and misalignments in the local maps. Furthermore, in
each sub-graph, a continuous-time representation of the sensor
trajectory allows to correct measurements between scan poses.
We evaluate our approach in multiple experiments by showing
qualitative results. Furthermore, we quantify the map quality
by an entropy-based measure.

Тэги:

#SLAM #Mapping #Robot #Laser #Lidar #Graph_Optimization #3D_modelling

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