Real-time 3D navigation for autonomous vision-guided MAVs
Shengdong Xu, Dominik Honegger, Marc Pollefeys, Lionel Heng
Abstract
Autonomous navigation of micro aerial vehicles (MAVs) in a-priori unknown environments is one of the most challenging problems in robotics. First, a MAV has to incrementally build a 3D geometric map from raw sensor data. Then, based on the mapping information, the path planner has to search for a cost-optimal trajectory to the goal in real-time. It is common practice to discretize the search space into a state lattice; by doing so, we reduce the path planning problem with differential constraints to a graph search problem that is easier to solve. However, a regular 3D state lattice requires a large amount of memory while graph search in a regular 3D state lattice incorporating numerous states is computationally intensive. In this paper, we introduce a novel path planning algorithm which extends the concept of a regular state lattice to an octree-based state lattice, and searches for an optimal trajectory in the octree-partitioned search space. Our octree-based state lattice representation discretizes large swathes of free space into few symbolic octants, and thus, encodes a significantly fewer number of states. As a result, memory consumption is kept to a minimum, and at the same time, graph search is made more efficient. Simulation experiments demonstrate the efficiency of path planning with an octree-based state lattice, and further field trials prove the viability of this path planning algorithm.
BibTeX
@inproceedings{iros2015_realtime3dnaviga,
title = {Real-time 3D navigation for autonomous vision-guided MAVs},
author = {Shengdong Xu and Dominik Honegger and Marc Pollefeys and Lionel Heng},
booktitle = {IROS 2015},
year = {2015}
}