# Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks

arXiv: https://arxiv.org/abs/1805.08689 · HTML: https://mlsanity.com/paper/1805.08689/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2018.

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

- [PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Data](https://mlsanity.com/paper/2105.04169/) (2021)
- [HFT: Lifting Perspective Representations via Hybrid Feature Transformation](https://mlsanity.com/paper/2204.05068/) (2022)
- [Roadside Lidar Vehicle Detection and Tracking Using Range And Intensity Background Subtraction](https://mlsanity.com/paper/2201.04756/) (2022)
- [Dynamic Conditional Imitation Learning for Autonomous Driving](https://mlsanity.com/paper/2211.11579/) (2022)
- [Lattice partition recovery with dyadic CART](https://mlsanity.com/paper/2105.13504/) (2021)
- [Improving Lidar-Based Semantic Segmentation of Top-View Grid Maps by Learning Features in Complementary Representations](https://mlsanity.com/paper/2203.01151/) (2022)

## Abstract (arXiv)

A detailed environment perception is a crucial component of automated vehicles. However, to deal with the amount of perceived information, we also require segmentation strategies. Based on a grid map environment representation, well-suited for sensor fusion, free-space estimation and machine learning, we detect and classify objects using deep convolutional neural networks. As input for our networks we use a multi-layer grid map efficiently encoding 3D range sensor information. The inference output consists of a list of rotated bounding boxes with associated semantic classes. We conduct extensive ablation studies, highlight important design considerations when using grid maps and evaluate our models on the KITTI Bird's Eye View benchmark. Qualitative and quantitative benchmark results show that we achieve robust detection and state of the art accuracy solely using top-view grid maps from range sensor data.