CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 2; 2026: 1.
4 papers.
- 2025SMART-OC: A Real-time Time-risk Optimal Replanning Algorithm for Dynamic Obstacles and Spatio-temporally Varying Currentscited by 1
- 2021MRRT: Multiple Rapidly-Exploring Random Trees for Fast Online Replanning in Dynamic Environmentscited by 0
- 2025SMART-3D: Three-Dimensional Self-Morphing Adaptive Replanning Treecited by 0
- 2026Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directionscited by 0
- 2026Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directions12 Jul 2026
- 2025SMART-3D: Three-Dimensional Self-Morphing Adaptive Replanning Tree20 Sep 2025
- 2025SMART-OC: A Real-time Time-risk Optimal Replanning Algorithm for Dynamic Obstacles and Spatio-temporally Varying Currents13 Aug 2025
- 2021MRRT: Multiple Rapidly-Exploring Random Trees for Fast Online Replanning in Dynamic Environments22 Apr 2021