CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees

TL;DR

This paper introduces CT-CPP, a novel layered 3D coverage path planning method for terrain reconstruction in obstacle-rich environments, optimizing traversal with a coverage tree to improve efficiency and accuracy.

Contribution

The paper presents a new CT-CPP approach that uses layered scanning and a coverage tree with TSP-inspired traversal for efficient 3D terrain reconstruction.

Findings

Significant reduction in trajectory length

Lower energy consumption

Improved reconstruction accuracy

Abstract

This letter addresses the 3D coverage path planning (CPP) problem for terrain reconstruction of unknown obstacle rich environments. Due to sensing limitations, the proposed method, called CT-CPP, performs layered scanning of the 3D region to collect terrain data, where the traveling sequence is optimized using the concept of a coverage tree (CT) with a TSP-inspired tree traversal strategy. The CT-CPP method is validated on a high-fidelity underwater simulator and the results are compared to an existing terrain following CPP method. The results show that CT-CPP yields significant reduction in trajectory length, energy consumption, and reconstruction error.