Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 6, Issue 4, Pages 7894-7901Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2021.3101856
Keywords
Aerial systems; perception and autonomy; motion and path planning
Categories
Funding
- Swiss National Science Foundation (SNSF) [PP00P2183720]
- NCCR Robotics
- Amazon Research Awards
- HILTI group
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The study proposes an online path-planning algorithm for fast exploration and 3D reconstruction of unknown areas, utilizing informed sampling techniques to achieve higher efficiency. Results show that the method outperforms existing exploration path planners in terms of speed and reconstruction quality.
As vision-based navigation of small aircraft has been demonstrated to reach relative maturity, research into effective path-planning algorithms to complete the loop of autonomous navigation has been booming. Although the literature has seen some impressive works in this area, efficient path-planning that can be used in tasks such as inspection and coverage is still an open problem. In this spirit, this letter presents an online path-planning algorithm for fast exploration and 3D reconstruction of a previously unknown area of interest. Micro Aerial Vehicles (MAVs) are an ideal candidate for this task due to their maneuverability, but their limited computational power and endurance require efficient planning strategies. Popular sampling-based methods randomly sample the MAV's configuration space and evaluate viewpoints according to their expected information gain. Most often, however, valuable resources are spent on information gain calculations of unpromising viewpoints. This letter proposes a novel informed sampling approach that leverages surface frontiers to sample viewpoints only where high information gain is expected, leading to faster exploration. We study the impact of informed sampling in a wide range of photo-realistic scenes, and we show that our approach outperforms state-of-the-art exploration path planners in terms of both speed and reconstruction quality.
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