In this section
@article{Vespa:2018:10.1109/LRA.2018.2792537,
author = {Vespa, E and Nikolov, N and Grimm, M and Nardi, L and Kelly, PH and Leutenegger, S},
doi = {10.1109/LRA.2018.2792537},
journal = {IEEE Robotics and Automation Letters},
pages = {1144--1151},
title = {Efficient octree-based volumetric SLAM supporting signed-distance and occupancy mapping},
url = {http://dx.doi.org/10.1109/LRA.2018.2792537},
volume = {3},
year = {2018}
}
TY - JOUR
AB - We present a dense volumetric simultaneous localisation and mapping (SLAM) framework that uses an octree representation for efficient fusion and rendering of either a truncated signed distance field (TSDF) or an occupancy map. The primary aim of this letter is to use one single representation of the environment that can be used not only for robot pose tracking and high-resolution mapping, but seamlessly for planning. We show that our highly efficient octree representation of space fits SLAM and planning purposes in a real-time control loop. In a comprehensive evaluation, we demonstrate dense SLAM accuracy and runtime performance on-par with flat hashing approaches when using TSDF-based maps, and considerable speed-ups when using occupancy mapping compared to standard occupancy maps frameworks. Our SLAM system can run at 10-40 Hz on a modern quadcore CPU, without the need for massive parallelization on a GPU. We, furthermore, demonstrate a probabilistic occupancy mapping as an alternative to TSDF mapping in dense SLAM and show its direct applicability to online motion planning, using the example of informed rapidly-exploring random trees (RRT).
AU - Vespa,E
AU - Nikolov,N
AU - Grimm,M
AU - Nardi,L
AU - Kelly,PH
AU - Leutenegger,S
DO - 10.1109/LRA.2018.2792537
EP - 1151
PY - 2018///
SN - 2377-3766
SP - 1144
TI - Efficient octree-based volumetric SLAM supporting signed-distance and occupancy mapping
T2 - IEEE Robotics and Automation Letters
UR - http://dx.doi.org/10.1109/LRA.2018.2792537
UR - http://hdl.handle.net/10044/1/55715
VL - 3
ER -