# GroundGrid:LiDAR Point Cloud Ground Segmentation and Terrain Estimation

arXiv: https://arxiv.org/abs/2405.15664 · HTML: https://mlsanity.com/paper/2405.15664/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 4 of 4)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systems](https://mlsanity.com/paper/2004.08467/) (2020)
- [Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud](https://mlsanity.com/paper/2207.11919/) (2022)
- [Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor](https://mlsanity.com/paper/2108.05560/) (2021)

## Built on by (most cited first, 14 of 14)

- [SPINE: Online Semantic Planning for Missions with Incomplete Natural Language Specifications in Unstructured Environments](https://mlsanity.com/paper/2410.03035/) (2024)
- [SSF: Sparse Long-Range Scene Flow for Autonomous Driving](https://mlsanity.com/paper/2501.17821/) (2025)
- [Air-Ground Collaboration for Language-Specified Missions in Unknown Environments](https://mlsanity.com/paper/2505.09108/) (2025)
- [DoGFlow: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidance](https://mlsanity.com/paper/2508.18506/) (2025)
- [Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities](https://mlsanity.com/paper/2505.09477/) (2025)
- [Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence](https://mlsanity.com/paper/2510.26915/) (2025)
- [PRAM-R: A Perception-Reasoning-Action-Memory Framework with LLM-Guided Modality Routing for Adaptive Autonomous Driving](https://mlsanity.com/paper/2603.04222/) (2026)
- [TerraSeg: Self-Supervised Ground Segmentation for Any LiDAR](https://mlsanity.com/paper/2603.27344/) (2026)
- [Can We Remove the Ground? Obstacle-aware Point Cloud Compression for Remote Object Detection](https://mlsanity.com/paper/2410.00582/) (2024)
- [FugSeg: Fast Uncertainty-aware Ground Segmentation for 3D Point Cloud](https://mlsanity.com/paper/2605.08952/) (2026)
- [GroundLoc: Efficient Large-Scale Outdoor LiDAR-Only Localization](https://mlsanity.com/paper/2510.24623/) (2025)
- [ACZ-GSeg: Adaptive Concentric Zone-based Two-stage Ground Segmentation for LiDAR Point Clouds](https://mlsanity.com/paper/2607.12110/) (2026)
- [The Coastline as a Structural Constraint: Harnessing Scene Geometry for Autonomous Surface Vessel Localization](https://mlsanity.com/paper/2608.21276/) (2026)
- [TriO: Tri-Modal Unsupervised Occupancy World Model for Anything Perception](https://mlsanity.com/paper/2609.32013/) (2026)

## Abstract (arXiv)

The precise point cloud ground segmentation is a crucial prerequisite of virtually all perception tasks for LiDAR sensors in autonomous vehicles. Especially the clustering and extraction of objects from a point cloud usually relies on an accurate removal of ground points. The correct estimation of the surrounding terrain is important for aspects of the drivability of a surface, path planning, and obstacle prediction. In this article, we propose our system GroundGrid which relies on 2D elevation maps to solve the terrain estimation and point cloud ground segmentation problems. We evaluate the ground segmentation and terrain estimation performance of GroundGrid and compare it to other state-of-the-art methods using the SemanticKITTI dataset and a novel evaluation method relying on airborne LiDAR scanning. The results show that GroundGrid is capable of outperforming other state-of-the-art systems with an average IoU of 94.78% while maintaining a high run-time performance of 171Hz. The source code is available at https://github.com/dcmlr/groundgrid