# A Tree-based Next-best-trajectory Method for 3D UAV Exploration

arXiv: https://arxiv.org/abs/2407.04386 · HTML: https://mlsanity.com/paper/2407.04386/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2024.

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

- [Why Sample Space Matters: Keyframe Sampling Optimization for LiDAR-based Place Recognition](https://mlsanity.com/paper/2410.02643/) (2024)
- [Next-Best-Trajectory Planning of Robot Manipulators for Effective Observation and Exploration](https://mlsanity.com/paper/2503.22588/) (2025)
- [Semantic Exploration and Dense Mapping of Complex Environments using Ground Robot with Panoramic LiDAR-Camera Fusion](https://mlsanity.com/paper/2505.22880/) (2025)
- [REST: Receding Horizon Explorative Steiner Tree for Zero-Shot Object-Goal Navigation](https://mlsanity.com/paper/2603.18624/) (2026)

## Abstract (arXiv)

This work presents a fully integrated tree-based combined exploration-planning algorithm: Exploration-RRT (ERRT). The algorithm is focused on providing real-time solutions for local exploration in a fully unknown and unstructured environment while directly incorporating exploratory behavior, robot-safe path planning, and robot actuation into the central problem. ERRT provides a complete sampling and tree-based solution for evaluating "where to go next" by considering a trade-off between maximizing information gain, and minimizing the distances travelled and the robot actuation along the path. The complete scheme is evaluated in extensive simulations, comparisons, as well as real-world field experiments in constrained and narrow subterranean and GPS-denied environments. The framework is fully ROS-integrated, straight-forward to use, and we open-source it at https://github.com/LTU-RAI/ExplorationRRT.