dfki-ric

dfki-ric / ground_segmentation

Public

A C++ library for ground segmentation of a pointcloud

dfki-ric.github.ioground_segmentation 3d-lidar autonomous-car autonomous-driving grid-map ground-detection
26
1
100% credibility
Found Feb 22, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
C++
AI Summary

This repository implements GSeg3D, a precise algorithm that separates ground surfaces from obstacles in 3D LiDAR scans for robots and autonomous vehicles.

How It Works

1
🔍 Discover GSeg3D

You hear about a smart tool that helps robots tell flat ground apart from obstacles using laser scans from self-driving cars or outdoor robots.

2
💻 Set up on your computer

You get the files ready on your Ubuntu machine by installing a few common helpers like building tools and math libraries.

3
🔨 Build the tools

You follow easy steps to compile everything, creating programs to test and visualize the ground detection.

4
📥 Grab a sample scan

You download a 3D laser scan file of a scene, like rocky terrain, to try it out.

5
✨ Run the magic

You launch the visual tool with your scan file and simple settings, and it quickly sorts the points into ground and obstacles.

6
👀 See the results

A window pops up showing safe green ground points and red obstacles, making it easy to check how well it works.

🚀 Robot ready to roll

Now your robot or self-driving project can reliably spot the ground, avoiding fake bumps and staying safe on any terrain.

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Star Growth

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AI-Generated Review

What is ground_segmentation?

This C++ library processes LiDAR point clouds to separate ground points from obstacles using a grid-based ground segmentation algorithm tuned for safety-critical apps like autonomous driving and robotics. You pass in a PCL point cloud and body-to-world orientation, tweak params like cell size and slope threshold, and get back two clouds: traversable ground and non-ground. A visual tool lets you test on PCD or PLY files right away, coloring ground green and obstacles red.

Why is it gaining traction?

It delivers 96-99% precision on SemanticKITTI benchmarks at 48ms runtime, beating alternatives in cluttered urban or unstructured terrain by prioritizing false-positive avoidance via dual-phase refinement and smart region expansion. Solid docs include YAML configs from the research paper, unit tests via GoogleTest, and CMake builds with GitHub Actions support—ideal for C++ GitHub repos targeting robotics. The separate ROS2 wrapper hooks into real pipelines fast.

Who should use this?

Perception engineers on outdoor mobile robots or self-driving cars needing reliable traversability maps before obstacle detection or path planning. Devs evaluating C++ libraries for point cloud math via PCL and Eigen, or prototyping ground segmentation in C++ GitHub projects.

Verdict

Promising for high-precision LiDAR ground removal, with strong tests, viz tool, and paper-backed results—but 11 stars and 1.0% credibility score signal early-stage maturity; run the visual demo on your data first. Worth a fork for custom robotics stacks.

(198 words)

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