# An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments

arXiv: https://arxiv.org/abs/1909.09548 · HTML: https://mlsanity.com/paper/1909.09548/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2019.

## Built on by (most cited first, 30 of 40)

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- [Deep Reinforcement Learning-based Large-scale Robot Exploration](https://mlsanity.com/paper/2403.10833/) (2024)
- [A Review on Viewpoints and Path-planning for UAV-based 3D Reconstruction](https://mlsanity.com/paper/2205.03716/) (2022)
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- [VL-TGS: Trajectory Generation and Selection using Vision Language Models in Mapless Outdoor Environments](https://mlsanity.com/paper/2408.02454/) (2024)
- [Active-Perceptive Motion Generation for Mobile Manipulation](https://mlsanity.com/paper/2310.00433/) (2023)
- [Learning Reconstructability for Drone Aerial Path Planning](https://mlsanity.com/paper/2209.10174/) (2022)
- [Active Neural Mapping at Scale](https://mlsanity.com/paper/2409.20276/) (2024)
- [FindAnything: Open-Vocabulary and Object-Centric Mapping for Robot Exploration in Any Environment](https://mlsanity.com/paper/2504.08603/) (2025)
- [SC-Explorer: Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning](https://mlsanity.com/paper/2208.08307/) (2022)
- [Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning](https://mlsanity.com/paper/2402.04894/) (2024)
- [IPPON: Common Sense Guided Informative Path Planning for Object Goal Navigation](https://mlsanity.com/paper/2410.19697/) (2024)
- [Semantically-driven Deep Reinforcement Learning for Inspection Path Planning](https://mlsanity.com/paper/2505.14443/) (2025)
- [A vision-based autonomous UAV inspection framework for unknown tunnel construction sites with dynamic obstacles](https://mlsanity.com/paper/2301.08422/) (2023)
- [Semantics-Aware Next-best-view Planning for Efficient Search and Detection of Task-relevant Plant Parts](https://mlsanity.com/paper/2306.09801/) (2023)
- [Probable Object Location (POLo) Score Estimation for Efficient Object Goal Navigation](https://mlsanity.com/paper/2311.07992/) (2023)
- [Active Visual Localization for Multi-Agent Collaboration: A Data-Driven Approach](https://mlsanity.com/paper/2310.02650/) (2023)
- [WildOS: Open-Vocabulary Object Search in the Wild](https://mlsanity.com/paper/2602.19308/) (2026)
- [Reinforcement Learning for Agile Active Target Sensing with a UAV](https://mlsanity.com/paper/2212.08214/) (2022)
- [LIME: Learning Intent-aware Camera Motion from Egocentric Video](https://mlsanity.com/paper/2607.02417/) (2026)
- [Autonomous Implicit Indoor Scene Reconstruction with Frontier Exploration](https://mlsanity.com/paper/2404.10218/) (2024)
- [Semantic-aware Next-Best-View for Multi-DoFs Mobile System in Search-and-Acquisition based Visual Perception](https://mlsanity.com/paper/2404.16507/) (2024)
- [SPACE: 3D Spatial Co-operation and Exploration Framework for Robust Mapping and Coverage with Multi-Robot Systems](https://mlsanity.com/paper/2411.02524/) (2024)
- [Sight Over Site: Perception-Aware Reinforcement Learning for Efficient Robotic Inspection](https://mlsanity.com/paper/2509.17877/) (2025)
- [Aerial Path Planning for Urban Geometry and Texture Co-Capture](https://mlsanity.com/paper/2509.22227/) (2025)
- [Dynamic Targeting of Satellite Observations Using Supplemental Geostationary Satellite Data and Hierarchical Planning](https://mlsanity.com/paper/2603.06719/) (2026)
- [Advanced Algorithms of Collision Free Navigation and Flocking for Autonomous UAVs](https://mlsanity.com/paper/2111.00166/) (2021)

## Abstract (arXiv)

The ability to plan informative paths online is essential to robot autonomy. In particular, sampling-based approaches are often used as they are capable of using arbitrary information gain formulations. However, they are prone to local minima, resulting in sub-optimal trajectories, and sometimes do not reach global coverage. In this paper, we present a new RRT*-inspired online informative path planning algorithm. Our method continuously expands a single tree of candidate trajectories and rewires segments to maintain the tree and refine intermediate trajectories. This allows the algorithm to achieve global coverage and maximize the utility of a path in a global context, using a single objective function. We demonstrate the algorithm's capabilities in the applications of autonomous indoor exploration as well as accurate Truncated Signed Distance Field (TSDF)-based 3D reconstruction on-board a Micro Aerial vehicle (MAV). We study the impact of commonly used information gain and cost formulations in these scenarios and propose a novel TSDF-based 3D reconstruction gain and cost-utility formulation. Detailed evaluation in realistic simulation environments show that our approach outperforms state of the art methods in these tasks. Experiments on a real MAV demonstrate the ability of our method to robustly plan in real-time, exploring an indoor environment solely with on-board sensing and computation. We make our framework available for future research.