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

arXiv: https://arxiv.org/abs/2010.09231 · HTML: https://mlsanity.com/paper/2010.09231/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [SMART-OC: A Real-time Time-risk Optimal Replanning Algorithm for Dynamic Obstacles and Spatio-temporally Varying Currents](https://mlsanity.com/paper/2508.09508/) (2025)
- [MRRT: Multiple Rapidly-Exploring Random Trees for Fast Online Replanning in Dynamic Environments](https://mlsanity.com/paper/2104.11059/) (2021)
- [SMART-3D: Three-Dimensional Self-Morphing Adaptive Replanning Tree](https://mlsanity.com/paper/2509.16812/) (2025)
- [Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directions](https://mlsanity.com/paper/2607.10649/) (2026)

## 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.