# Dynamic Occupancy Grids for Object Detection: A Radar-Centric Approach

arXiv: https://arxiv.org/abs/2402.01488 · HTML: https://mlsanity.com/paper/2402.01488/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2024.

## Built on by (most cited first, 3 of 3)

- [4D-ROLLS: 4D Radar Occupancy Learning via LiDAR Supervision](https://mlsanity.com/paper/2505.13905/) (2025)
- [SpaRC-AD: A Baseline for Radar-Camera Fusion in End-to-End Autonomous Driving](https://mlsanity.com/paper/2508.10567/) (2025)
- [IRGNN: Efficient Invariant Radar Graph Neural Network for Radar Point Cloud Object Detection](https://mlsanity.com/paper/2608.14394/) (2026)

## Abstract (arXiv)

Dynamic Occupancy Grid Mapping is a technique used to generate a local map of the environment containing both static and dynamic information. Typically, these maps are primarily generated using lidar measurements. However, with improvements in radar sensing, resulting in better accuracy and higher resolution, radar is emerging as a viable alternative to lidar as the primary sensor for mapping. In this paper, we propose a radar-centric dynamic occupancy grid mapping algorithm with adaptations to the state computation, inverse sensor model, and field-of-view computation tailored to the specifics of radar measurements. We extensively evaluate our approach using real data to demonstrate its effectiveness and establish the first benchmark for radar-based dynamic occupancy grid mapping using the publicly available Radarscenes dataset.