# Long-Term Robot Navigation in Indoor Environments Estimating Patterns in Traversability Changes

arXiv: https://arxiv.org/abs/1909.12733 · HTML: https://mlsanity.com/paper/1909.12733/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2019.

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

- [Risk-Conditioned Distributional Soft Actor-Critic for Risk-Sensitive Navigation](https://mlsanity.com/paper/2104.03111/) (2021)
- [Conflict Avoidance in Social Navigation -- a Survey](https://mlsanity.com/paper/2106.12113/) (2021)
- [Spot the Difference: A Novel Task for Embodied Agents in Changing Environments](https://mlsanity.com/paper/2204.08502/) (2022)
- [A Finite-State Controller Based Offline Solver for Deterministic POMDPs](https://mlsanity.com/paper/2505.00596/) (2025)
- [Perpetua: Multi-Hypothesis Persistence Modeling for Semi-Static Environments](https://mlsanity.com/paper/2507.18808/) (2025)
- [A New Lightweight Hybrid Graph Convolutional Neural Network -- CNN Scheme for Scene Classification using Object Detection Inference](https://mlsanity.com/paper/2407.14658/) (2024)

## Abstract (arXiv)

Nowadays, mobile robots are deployed in many indoor environments, such as offices or hospitals. These environments are subject to changes in the traversability that often happen by following repeating patterns. In this paper, we investigate the problem of navigating in such environments over extended periods of time by capturing these patterns and exploiting this knowledge to make informed decisions. Our approach incrementally estimates a model of the traversability changes from robot's observations and uses a probabilistic graphical model to make predictions at currently unobserved locations. In the belief space defined by the predictions, we plan paths that trade off the risk to encounter obstacles and the information gain of visiting unknown locations. We implemented our approach and tested it in different indoor environments. The experiments suggest that in the long run, our approach leads to navigation along shorter paths compared to following a greedy shortest path policy.