# History-aware Autonomous Exploration in Confined Environments using MAVs

arXiv: https://arxiv.org/abs/1803.10558 · HTML: https://mlsanity.com/paper/1803.10558/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2018.

## Built on by (most cited first, 6 of 6)

- [SC-Explorer: Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning](https://mlsanity.com/paper/2208.08307/) (2022)
- [IPPON: Common Sense Guided Informative Path Planning for Object Goal Navigation](https://mlsanity.com/paper/2410.19697/) (2024)
- [COMRES-VLM: Coordinated Multi-Robot Exploration and Search using Vision Language Models](https://mlsanity.com/paper/2509.26324/) (2025)
- [Autonomous Implicit Indoor Scene Reconstruction with Frontier Exploration](https://mlsanity.com/paper/2404.10218/) (2024)
- [OTO Planner: An Efficient Only Travelling Once Exploration Planner for Complex and Unknown Environments](https://mlsanity.com/paper/2406.07294/) (2024)
- [Information Gain Is Not All You Need](https://mlsanity.com/paper/2504.01980/) (2025)

## Abstract (arXiv)

Many scenarios require a robot to be able to explore its 3D environment online without human supervision. This is especially relevant for inspection tasks and search and rescue missions. To solve this high-dimensional path planning problem, sampling-based exploration algorithms have proven successful. However, these do not necessarily scale well to larger environments or spaces with narrow openings. This paper presents a 3D exploration planner based on the principles of Next-Best Views (NBVs). In this approach, a Micro-Aerial Vehicle (MAV) equipped with a limited field-of-view depth sensor randomly samples its configuration space to find promising future viewpoints. In order to obtain high sampling efficiency, our planner maintains and uses a history of visited places, and locally optimizes the robot's orientation with respect to unobserved space. We evaluate our method in several simulated scenarios, and compare it against a state-of-the-art exploration algorithm. The experiments show substantial improvements in exploration time (2× faster), computation time, and path length, and advantages in handling difficult situations such as escaping dead-ends (up to 20× faster). Finally, we validate the on-line capability of our algorithm on a computational constrained real world MAV.