Paper Info
Title
GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous Vehicles
Abstract
Ground plane estimation and ground point segmentation is a crucial precursor for many applications in robotics and intelligent vehicles like navigable space detection and occupancy grid generation, 3D object detection, point cloud matching for localization and registration for mapping. In this paper, we present GndNet, a novel end-to-end approach that estimates the ground plane elevation information in a grid-based representation and segments the ground points simultaneously in real-time. GndNet uses PointNet and Pillar Feature Encoding network to extract features and regresses ground height for each cell of the grid. We augment the SemanticKITTI dataset to train our network. We demonstrate qualitative and quantitative evaluation of our results for ground elevation estimation and semantic segmentation of point cloud. GndNet establishes a new state-of-the-art, achieves a run-time of 55Hz for ground plane estimation and ground point segmentation.
Year
DOI
Venue
2020
10.1109/IROS45743.2020.9340979
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Keywords
DocType
ISSN
GndNet,fast ground plane estimation,point cloud segmentation,ground point segmentation,navigable space detection,occupancy grid generation,ground plane elevation information,grid-based representation,ground points,regresses ground height,ground elevation estimation,frequency 55.0 Hz
Conference
2153-0858
ISBN
Citations 
PageRank 
978-1-7281-6213-3
1
0.39
References 
Authors
0
4
Name
Order
Citations
PageRank
Anshul K. Paigwar163.53
Özgür Erkent2113.20
David S. González3335.84
Christian Laugier461.79