We present a computationally inexpensive RGBD-SLAM solution taylored to the application on autonomous MAVs, which enables our MAV to fly in an unknown environment and create a map of its surroundings completely autonomously, with all computations running on its onboard computer. We achieve this by implementing efficient methods for both tracking its current location with respect to a heavily processed previously seen RGBD image (keyframe) and efficient relative registration of a set of keyframes using bundle adjustment with depth constraints as a front-end for pose graph optimization. We prove the accuracy and efficiency of our system based on a public benchmark dataset and demonstrate that the proposed method enables our quadrotor to fly autonomously.
[pdf]
@inproceedings{scherer2013iros,
author = {Scherer, Sebastian A. and Andreas Zell},
title = {{Efficient Onboard RGBD-SLAM for Fully Autonomous MAVs}},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS 2013)},
year = {2013},
address = {Tokyo Big Sight, Japan},
month = {November},
abstract = {We present a computationally inexpensive RGBD-SLAM solution taylored
to the application on autonomous MAVs, which enables our MAV to fly
in an unknown environment and create a map of its surroundings completely
autonomously, with all computations running on its onboard computer.
We achieve this by implementing efficient methods for both tracking
its current location with respect to a heavily processed previously
seen RGBD image (keyframe) and efficient relative registration of
a set of keyframes using bundle adjustment with depth constraints
as a front-end for pose graph optimization. We prove the accuracy
and efficiency of our system based on a public benchmark dataset
and demonstrate that the proposed method enables our quadrotor to
fly autonomously.},
pdf = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2013/scherer2013iros.pdf},
}