An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments

By Lukas Maximilian Schmid, Michael Pantic, Raghav Khanna, Lionel Ott, Roland Yves Siegwart, Juan I. Nieto. IEEE Robotics and Automation Letters, 2019

302 citations recorded by the source corpus.

The ability to plan informative paths online is essential to robot autonomy. In particular, sampling-based approaches are often used as they are capable of using arbitrary information gain formulations. However, they are prone to local minima, resulting in sub-optimal trajectories, and sometimes do not reach global coverage. In this letter, we present a new RRT*-inspired online informative path planning algorithm. Our method continuously expands a single tree of candidate trajectories and rewires nodes to maintain the tree and refine intermediate paths. This allows the algorithm to achieve global coverage and maximize the utility of a path in a global context, using a single objective function. We demonstrate the algorithm's capabilities in the applications of autonomous indoor exploration as well as accurate Truncated Signed Distance Field (TSDF)-based 3D reconstruction on-board a Micro Aerial Vehicle (MAV). We study the impact of commonly used information gain and cost formulations in these scenarios and propose a novel TSDF-based 3D reconstruction gain and cost-utility formulation. Detailed evaluation in realistic simulation environments show that our approach outperforms sampling-based state of the art methods in these tasks. Experiments on a real MAV demonstrate the ability of our method to robustly plan in real-time, exploring an indoor environment with on-board sensing and computation. We make our framework available for future research.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/6d888a1ef4a780c8f6618fce638a58e6f236c916

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Citations in the corpus

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms (2021, methodology): “[147] studied the potential influence of”
FUEL: Fast UAV Exploration Using Incremental Frontier Structure and Hierarchical Planning (2020, methodology): “[2] maintains and refines a single tree continuously using a rewiring scheme inspired by RRT*.”