# Accelerating Point Cloud Ground Segmentation: From Mechanical to Solid-State Lidars

arXiv: https://arxiv.org/abs/2408.10404 · HTML: https://mlsanity.com/paper/2408.10404/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation](https://mlsanity.com/paper/2103.14962/) (2021)
- [Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor](https://mlsanity.com/paper/2108.05560/) (2021)
- [Towards High-Performance Solid-State-LiDAR-Inertial Odometry and Mapping](https://mlsanity.com/paper/2010.13150/) (2020)
- [ACSC: Automatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera Systems](https://mlsanity.com/paper/2011.08516/) (2020)
- [ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA](https://mlsanity.com/paper/1808.03506/) (2018)

## Abstract (arXiv)

In this study, we propose a novel parallel processing method for point cloud ground segmentation, aimed at the technology evolution from mechanical to solid-state Lidar (SSL). We first benchmark point-based, grid-based, and range image-based ground segmentation algorithms using the SemanticKITTI dataset. Our results indicate that the range image-based method offers superior performance and robustness, particularly in resilience to frame slicing. Implementing the proposed algorithm on an FPGA demonstrates significant improvements in processing speed and scalability of resource usage. Additionally, we develop a custom dataset using camera-SSL equipment on our test vehicle to validate the effectiveness of the parallel processing approach for SSL frames in real world, achieving processing rates up to 30.9 times faster than CPU implementations. These findings underscore the potential of parallel processing strategies to enhance Lidar technologies for advanced perception tasks in autonomous vehicles and robotics. The data and code will be available post-publication on our GitHub repository: urlhttps://github.com/WPI-APA-Lab/GroundSeg-Solid-State-Lidar-Parallel-Processing