# Occupancy Grids: A Stochastic Spatial Representation for Active Robot Perception

arXiv: https://arxiv.org/abs/1304.1098 · HTML: https://mlsanity.com/paper/1304.1098/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in World models for autonomous driving, one of 11 directions in World models.
- First posted on arXiv 27 Mar 2013.

## Placed in

- World models / [World models for autonomous driving](https://mlsanity.com/wm/world-models-for-autonomous-driving/)

## Built on by (most cited first, 10 of 10)

- [Bird's-Eye-View Scene Graph for Vision-Language Navigation](https://mlsanity.com/paper/2308.04758/) (2023)
- [Volumetric Environment Representation for Vision-Language Navigation](https://mlsanity.com/paper/2403.14158/) (2024)
- [LaRa: Latents and Rays for Multi-Camera Bird's-Eye-View Semantic Segmentation](https://mlsanity.com/paper/2206.13294/) (2022)
- [ViT-BEVSeg: A Hierarchical Transformer Network for Monocular Birds-Eye-View Segmentation](https://mlsanity.com/paper/2205.15667/) (2022)
- [Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots](https://mlsanity.com/paper/2507.20217/) (2025)
- [Fisheye Camera and Ultrasonic Sensor Fusion For Near-Field Obstacle Perception in Bird's-Eye-View](https://mlsanity.com/paper/2402.00637/) (2024)
- [AdaVLN: Towards Visual Language Navigation in Continuous Indoor Environments with Moving Humans](https://mlsanity.com/paper/2411.18539/) (2024)
- [GAD-Generative Learning for HD Map-Free Autonomous Driving](https://mlsanity.com/paper/2405.00515/) (2024)
- [A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles](https://mlsanity.com/paper/2505.07855/) (2025)
- [SR-Nav: Spatial Relationships Matter for Zero-shot Object Goal Navigation](https://mlsanity.com/paper/2603.18443/) (2026)

## Abstract (arXiv)

In this paper we provide an overview of a new framework for robot perception, real-world modelling, and navigation that uses a stochastic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field model that maintains probabilistic estimates of the occupancy state of each cell in a spatial lattice. Bayesian estimation mechanisms employing stochastic sensor models allow incremental updating of the Occupancy Grid using multi-view, multi-sensor data, composition of multiple maps, decision-making, and incorporation of robot and sensor position uncertainty. We present the underlying stochastic formulation of the Occupancy Grid framework, and discuss its application to a variety of robotic tusks. These include range-based mapping, multi-sensor integration, path-planning and obstacle avoidance, handling of robot position uncertainty, incorporation of pre-compiled maps, recovery of geometric representations, and other related problems. The experimental results show that the Occupancy Grid approach generates dense world models, is robust under sensor uncertainty and errors, and allows explicit handling of uncertainty. It supports the development of robust and agile sensor interpretation methods, incremental discovery procedures, and composition of information from multiple sources. Furthermore, the results illustrate that robotic tasks can be addressed through operations performed di- rectly on the Occupancy Grid, and that these operations have strong parallels to operations performed in the image processing domain.