# Map-Predictive Motion Planning in Unknown Environments

arXiv: https://arxiv.org/abs/1910.08184 · HTML: https://mlsanity.com/paper/1910.08184/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2019.

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

- [Learning Active Camera for Multi-Object Navigation](https://mlsanity.com/paper/2210.07505/) (2022)
- [Chat2Map: Efficient Scene Mapping from Multi-Ego Conversations](https://mlsanity.com/paper/2301.02184/) (2023)
- [4CNet: A Diffusion Approach to Map Prediction for Decentralized Multi-Robot Exploration](https://mlsanity.com/paper/2402.17904/) (2024)
- [Train-Once Plan-Anywhere Kinodynamic Motion Planning via Diffusion Trees](https://mlsanity.com/paper/2508.21001/) (2025)
- [SePaint: Semantic Map Inpainting via Multinomial Diffusion](https://mlsanity.com/paper/2303.02737/) (2023)
- [Kinodynamic Motion Planning for Mobile Robot Navigation across Inconsistent World Models](https://mlsanity.com/paper/2509.26339/) (2025)

## Abstract (arXiv)

Algorithms for motion planning in unknown environments are generally limited in their ability to reason about the structure of the unobserved environment. As such, current methods generally navigate unknown environments by relying on heuristic methods to choose intermediate objectives along frontiers. We present a unified method that combines map prediction and motion planning for safe, time-efficient autonomous navigation of unknown environments by dynamically-constrained robots. We propose a data-driven method for predicting the map of the unobserved environment, using the robot's observations of its surroundings as context. These map predictions are then used to plan trajectories from the robot's position to the goal without requiring frontier selection. We demonstrate that our map-predictive motion planning strategy yields a substantial improvement in trajectory time over a naive frontier pursuit method and demonstrates similar performance to methods using more sophisticated frontier selection heuristics with significantly shorter computation time.