We address the problem of on-line volumentric map creation of unknown environments and planning of safe trajectories. The sensor used for this purpose is a stereo camera. Our system is designed to work in GPS denied areas. We design a keyframe based hybrid SLAM algorithm which combines feature-based stereo SLAM and direct stereo SLAM. We use it to grow the map while keeping track of the camera pose in the map. The SLAM system builds a sparse map. For the path planning we build a dense volumetric map by computing dense stereo matching at keyframes and inserting the point clouds in an Octomap. The computed disparity maps are reused on the direct tracking refinement step of our hybrid SLAM. Safe trajectories are then estimated using the RRTstar algorithm in the SE(3) state space. In our experiments, we show that we can map large environments with hundreds of keyframes. We also conducted autonomous outdoor flights using a quadcopter to validate our approach for obstacle avoidance.
@inproceedings{JellalECMR2017,
title = {Outdoor Obstacle Avoidance based on Hybrid Stereo Visual SLAM for an Autonomous Quadrotor MAV},
author = {Ait Jellal, Radouane and Zell, Andreas},
booktitle = {IEEE 8th European Conference on Mobile Robots (ECMR)},
year = {2017},
address = {ENSTA ParisTech, Université Paris-Saclay, France},
month = sept,
abstract = {We address the problem of on-line volumentric
map creation of unknown environments and planning of safe
trajectories. The sensor used for this purpose is a stereo camera.
Our system is designed to work in GPS denied areas. We design
a keyframe based hybrid SLAM algorithm which combines
feature-based stereo SLAM and direct stereo SLAM. We use
it to grow the map while keeping track of the camera pose in
the map. The SLAM system builds a sparse map. For the path
planning we build a dense volumetric map by computing dense
stereo matching at keyframes and inserting the point clouds
in an Octomap. The computed disparity maps are reused on
the direct tracking refinement step of our hybrid SLAM. Safe
trajectories are then estimated using the RRTstar algorithm in
the SE(3) state space. In our experiments, we show that we can
map large environments with hundreds of keyframes. We also
conducted autonomous outdoor flights using a quadcopter to
validate our approach for obstacle avoidance.},
days = {6-8},
note = {},
pdf = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2017/JellalECMR17.pdf}
}