# Mapping LiDAR and Camera Measurements in a Dual Top-View Grid Representation Tailored for Automated Vehicles

arXiv: https://arxiv.org/abs/2204.07887 · HTML: https://mlsanity.com/paper/2204.07887/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 16 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [GA-Net: Guided Aggregation Net for End-to-end Stereo Matching](https://mlsanity.com/paper/1904.06587/) (2019)
- [Hierarchical Neural Architecture Search for Deep Stereo Matching](https://mlsanity.com/paper/2010.13501/) (2020)
- [Improving Semantic Segmentation via Video Propagation and Label Relaxation](https://mlsanity.com/paper/1812.01593/) (2018)
- [ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation](https://mlsanity.com/paper/2012.05258/) (2020)
- [Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes](https://mlsanity.com/paper/2101.06085/) (2021)
- [A Simulation-based End-to-End Learning Framework for Evidential Occupancy Grid Mapping](https://mlsanity.com/paper/2102.12718/) (2021)
- [Fusing Laser Scanner and Stereo Camera in Evidential Grid Maps](https://mlsanity.com/paper/1805.10046/) (2018)
- [Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines](https://mlsanity.com/paper/2203.01180/) (2022)

## Built on by (most cited first, 1 of 1)

- [Sensor Data Fusion in Top-View Grid Maps using Evidential Reasoning with Advanced Conflict Resolution](https://mlsanity.com/paper/2204.08780/) (2022)

## Abstract (arXiv)

We present a generic evidential grid mapping pipeline designed for imaging sensors such as LiDARs and cameras. Our grid-based evidential model contains semantic estimates for cell occupancy and ground separately. We specify the estimation steps for input data represented by point sets, but mainly focus on input data represented by images such as disparity maps or LiDAR range images. Instead of relying on an external ground segmentation only, we deduce occupancy evidence by analyzing the surface orientation around measurements. We conduct experiments and evaluate the presented method using LiDAR and stereo camera data recorded in real traffic scenarios. Our method estimates cell occupancy robustly and with a high level of detail while maximizing efficiency and minimizing the dependency to external processing modules.