GSeg3D

High-Precision Grid-Based Ground Segmentation for Safety-Critical Autonomous Driving and Robotics Applications

This module implements the core algorithmic components of GSeg3D, a high-precision, grid-based ground segmentation method for LiDAR point clouds designed for safety-critical autonomous driving and robotics applications.
It follows the methodology described in the paper:

‍GSeg3D: A High-Precision Grid-Based Algorithm for Safety-Critical Ground Segmentation in LiDAR Point Clouds
Muhammad Haider Khan Lodhi and Christoph Hertzberg
Proceedings of the 7th International Conference on Robotics and Computer Vision (ICRCV),
Hong Kong, China, October 24–26, 2025.
IEEE, 2025.
DOI: https://doi.org/10.1109/ICRCV67407.2025.11349133

Please find below a summary comparison of the average results on the SemanticKITTI sequences 00–10:

ROS2

The official ROS 2 wrapper for GSeg3D is available here:

ROS 2 Node Implementation

Motivation

Reliable ground segmentation is a fundamental prerequisite for:

• Obstacle detection
• Traversability estimation
• Navigation and planning
• Mapping and localization

False positives (classifying ground as obstacles) can directly compromise safety and downstream decision-making.
GSeg3D is explicitly designed to maximize precision while maintaining robust recall, even in cluttered and unstructured environments.

Algorithm Overview

GSeg3D paper performs two-phase grid-based ground segmentation. For a reference implementation of the two-phase approach, see the ROS 2 node available here.

<a href="https://github.com/dfki-ric/ground_segmentation_ros2/blob/c370b2be20c75dc9fb9e6710f933ab1328fa0981/src/ground_segmentation_ros2_node.cpp#L84C8-L84C29" >Phase I – Coarse Segmentation</a>

• Uses larger vertical grid cells
• Aggressively captures elevated structures as non-ground
• Ensures high initial precision
• May temporarily over-segment ground in cluttered areas

<a href="https://github.com/dfki-ric/ground_segmentation_ros2/blob/c370b2be20c75dc9fb9e6710f933ab1328fa0981/src/ground_segmentation_ros2_node.cpp#L91C8-L91C30" >Phase II – Refinement</a>

• Uses smaller vertical grid cells
• Re-evaluates ground points from Phase I
• Corrects false positives and false negatives from Phase I
• Enforces vertical consistency constraints

This dual-phase strategy strikes a strong balance between precision and recall, a trade-off that is especially important in safety-critical systems.

Processing Pipeline

Each phase follows the same four core steps:

1. Grid Representation
   • The point cloud is discretized into a regular 3D grid
   • Each point is assigned to a cell based on configurable cell sizes
2. Local Eigen Classification
   • Per-cell covariance matrix and eigen decomposition
   • Cells classified as:
     • LINE (dominant linear structure)
     • PLANE (surface-like structure)
     • NOISE (scattered points)
3. Surface Gradient Analysis
   • Robust plane fitting using PCL SACSegmentation (PROSAC)
   • Slope estimation relative to gravity (world frame)
   • Planar cells exceeding slope thresholds are rejected as ground
4. Ground Region Expansion
   • Candidate ground cell centroids are indexed using a KD-tree (nanoflann)
   • Radius-based neighborhood expansion ensures connectivity
   • Works even when grid resolution is finer than LiDAR scan-line spacing
   • Phase II additionally enforces height-difference constraints

Key Design Contributions

KD-Tree–Based Ground Expansion

Traditional grid-neighbor expansion fails at high grid resolutions due to scan-line gaps.
GSeg3D overcomes this by:

• Indexing ground centroids in a KD-tree
• Performing radius-based spatial expansion
• Enabling fine grid resolutions without loss of connectivity or recall

Robust Seed Initialization

• Synthetic seed points are injected directly beneath the robot
• Ensures reliable ground initialization even with occlusions or sparse data
• Synthetic points are removed before final output and evaluation

Multi-Step Ground Verification

Each candidate cell undergoes:

• Plane inlier / outlier separation
• Bounding-box sparsity analysis
• Neighborhood height consistency checks
• Floating-cell rejection (non-ground below)

Only physically plausible and spatially coherent cells are labeled as ground.

Implementation Details

To generate the HTML documentation for this library:

System Requirements

OS: Ubuntu 22.04, Ubuntu 24.04

Dependencies

• CMake
• PCL
  
• Eigen3
  
• GoogleTest
  
• NanoFlann
• JDK 17

Example (Ubuntu):

sudo apt update
sudo apt install cmake libpcl-dev libeigen3-dev libgtest-dev libnanoflann-dev openjdk-17-jre

Build Instructions

git clone git@github.com:dfki-ric/ground_segmentation.git
cd ground_segmentation && mkdir -p build
cd build
cmake .. -DCMAKE_BUILD_TYPE=RELEASE
make

Generating Documentation

1. Make sure you have Doxygen and Graphviz installed:

sudo apt install doxygen graphviz

1. From the root of the project, run CMake and build the doc target:

cmake --build . --target doc

1. After building, open the generated HTML documentation doc/html/index.html in your browser

‍The main page will include the README and automatically list the header files and classes.

Running Unit Tests

ctest --test-dir test/ --output-on-failure

or

./test/test_pointcloud_grid

Visual Ground Segmentation Tool

./test/test_visual_segmentation <cloud.pcd|cloud.ply> [cell_size] [slope_deg] [dist_to_ground]

Example

wget https://zenodo.org/records/13771864/files/utah.ply
./test/test_visual_segmentation utah.ply 1.0 30 -0.5

Colour Coding

• Green → Ground points
  
• Red → Obstacles / non-ground points
  

Close the viewer window to exit.

Core Class

template<typename PointT>
class PointCloudGrid;

Main API

PointCloudGrid(const GridConfig& config);
 
void setInputCloud(
    pcl::PointCloud<PointT>::Ptr input,
    const Eigen::Quaterniond& R_body2World
);
 
std::pair<
    pcl::PointCloud<PointT>::Ptr,
    pcl::PointCloud<PointT>::Ptr
> segmentPoints();

Typical Configuration

Below is a recommended baseline setup for most outdoor mobile robotics use cases (e.g., LiDAR-based UGVs). Adjust values based on sensor height, terrain roughness, and map resolution.

Phase-I

cellSizeX: 1.0
cellSizeY: 1.0
cellSizeZ: 10.0   # Phase I
slopeThresholdDegrees: 20.0
groundInlierThreshold: 0.125
centroidSearchRadius: 5.0
distToGround: -1.723 # (e.g. SemanticKITTI)
maxGroundHeightDeviation: 0.1

Phase-II

After coarse ground detection of Phase-I, Phase II increases vertical resolution to refine classification. All parameters are kept same as Phase-I expect for the cellSizeZ

cellSizeZ: 1.0

Performance Summary (SemanticKITTI)

• Precision: ~96–99% (low variance)
• Recall: ~85–91%
• F1-score: ~92–94%
• Runtime: ~48 ms (CPU, single scan average)

GSeg3D consistently demonstrates stable, high-precision performance across:

• Urban environments
• Highways
• Unstructured terrain

Intended Use

• Safety-critical autonomous driving
• Outdoor mobile robotics
• Traversability analysis
• Mapping and perception pipelines

Notes & Future Work

• Recall degradation in dense vegetation remains challenging
• Planned extensions:
  • Semantic-aware refinement
  • Temporal fusion
  • GPU acceleration

Tests & Visualisation

This directory contains unit tests and a visual debugging tool for the PointCloudGrid ground segmentation implementation.

The code uses PCL, Eigen, and GoogleTest and is intended for quick validation of grid indexing, ground classification logic, and phase-based segmentation behaviour.

Contents

• test_pointcloud_grid.cpp
  GoogleTest-based unit tests covering:
  • Grid indexing and hashing
  • Flat ground detection
  • Vertical structures as obstacles
  • Phase 1 vs Phase 2 behaviour
  • Orientation / gravity influence
  • Robustness against empty inputs
• test_visual_segmentation.cpp
  A visual inspection tool that:
  • Loads a .pcd or .ply point cloud
  • Runs phase 1 + phase 2 ground segmentation
  • Displays ground (green) and obstacles (red) using PCLVisualizer

Notes

• Phase 1 allows vertical and slanted propagation from the robot seed cell.
• Phase 2 refines ground classification using only previously detected ground.
• Orientation (Eigen::Quaterniond) is interpreted as sensor → world rotation.

License

BSD-3 Clause License.

Citation

If you use this work, please cite:

Muhammad Haider Khan Lodhi and Christoph Hertzberg, "GSeg3D: A High-Precision Grid-Based Algorithm for Safety-Critical Ground Segmentation in LiDAR Point Clouds" in 2025 7th International Conference on Robotics and Computer Vision (ICRCV), pp. 119-126, 2025. doi: 10.1109/ICRCV67407.2025.11349133

© DFKI Robotics Innovation Center