Proceedings ArticleDOI
Fast segmentation of 3D point clouds for ground vehicles
Michael Himmelsbach,Felix von Hundelshausen,Hans-Joachim Wuensche +2 more
- pp 560-565
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TLDR
A fast method for segmentation of large-size long-range 3D point clouds that especially lends itself for later classification of objects that requires less runtime while at the same time yielding segmentation results that are better suited forLater classification of the identified objects.Abstract:
This paper describes a fast method for segmentation of large-size long-range 3D point clouds that especially lends itself for later classification of objects. Our approach is targeted at high-speed autonomous ground robot mobility, so real-time performance of the segmentation method plays a critical role. This is especially true as segmentation is considered only a necessary preliminary for the more important task of object classification that is itself computationally very demanding. Efficiency is achieved in our approach by splitting the segmentation problem into two simpler subproblems of lower complexity: local ground plane estimation followed by fast 2D connected components labeling. The method's performance is evaluated on real data acquired in different outdoor scenes, and the results are compared to those of existing methods. We show that our method requires less runtime while at the same time yielding segmentation results that are better suited for later classification of the identified objects.read more
Citations
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Proceedings ArticleDOI
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain
Tixiao Shan,Brendan Englot +1 more
TL;DR: A lightweight and ground-optimized lidar odometry and mapping method, LeGO-LOAM, for realtime six degree-of-freedom pose estimation with ground vehicles and integrated into a SLAM framework to eliminate the pose estimation error caused by drift is integrated.
Proceedings ArticleDOI
3D fully convolutional network for vehicle detection in point cloud
TL;DR: The fully convolutional network based detection techniques to 3D and apply to point cloud data is extended and verified on the task of vehicle detection from lidar point cloud for autonomous driving.
Proceedings ArticleDOI
Vehicle Detection from 3D Lidar Using Fully Convolutional Network
Bo Li,Zhang Tianlei,Xia Tian +2 more
TL;DR: In this article, a 2D point map and a single 2D end-to-end fully convolutional network are used to predict the objectness confidence and the bounding boxes simultaneously.
Proceedings ArticleDOI
Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications
TL;DR: The proposed algorithm first extracts the ground surface in an iterative fashion using deterministically assigned seed points, and then clusters the remaining non-ground points taking advantage of the structure of the LiDAR point cloud.
Proceedings ArticleDOI
Fast range image-based segmentation of sparse 3D laser scans for online operation
TL;DR: This paper presents a fast method that segments 3D range data into different objects, runs online, and has small computational demands that can operate at over 100 Hz for the 64-beam Velodyne scanner on a single core of a mobile CPU while producing high quality segmentation results.
References
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Chris Urmson,Joshua Anhalt,Drew Bagnell,Christopher R. Baker,Robert Bittner,Michael Clark,John M. Dolan,D Duggins,Tugrul Galatali,Christopher Geyer,Michele Gittleman,Sam Harbaugh,Martial Hebert,Thomas M. Howard,Sascha Kolski,Alonzo Kelly,Maxim Likhachev,Matthew McNaughton,Nick Miller,Kevin Peterson,Brian Pilnick,Ragunathan Rajkumar,Paul E. Rybski,Bryan Salesky,Young-Woo Seo,Sanjiv Singh,Jarrod M. Snider,Anthony Stentz,William Whittaker,Ziv Wolkowicki,Jason Ziglar,Hong Bae,Thomas G. Brown,Daniel Demitrish,Bakhtiar Brian Litkouhi,Jim Nickolaou,Varsha Sadekar,Wende Zhang,Joshua Struble,Michael Taylor,Michael Darms,Dave Ferguson +41 more
TL;DR: This dissertation aims to provide a history of web exceptionalism from 1989 to 2002, a period chosen in order to explore its roots as well as specific cases up to and including the year in which descriptions of “Web 2.0” began to circulate.
Journal IssueDOI
Junior: The Stanford entry in the Urban Challenge
Michael Montemerlo,Jan Becker,Suhrid Bhat,Hendrik Dahlkamp,Dmitri A. Dolgov,Scott M. Ettinger,Dirk Haehnel,Tim Hilden,Gabe Hoffmann,Burkhard Huhnke,Doug Johnston,Stefan Klumpp,Dirk Langer,Anthony Levandowski,Jesse Levinson,Julien Marcil,David Orenstein,Johannes Paefgen,Isaac Penny,Anna Petrovskaya,Mike Pflueger,Ganymed Stanek,David Stavens,Antone Vogt,Sebastian Thrun +24 more
TL;DR: The architecture of Junior, a robotic vehicle capable of navigating urban environments autonomously, is presented, which successfully finished and won second place in the DARPA Urban Challenge, a robot competition organized by the U.S. Government.
Junior: The Stanford Entry in the Urban Challenge.
Michael Montemerlo,Jan Becker,Suhrid Bhat,Hendrik Dahlkamp,Dmitri A. Dolgov,Scott M. Ettinger,Dirk Hähnel,Tim Hilden,Gabe Hoffmann,Burkhard Huhnke,Doug Johnston,Stefan Klumpp,Dirk Langer,Anthony Levandowski,Jesse Levinson,Julien Marcil,David Orenstein,Johannes Paefgen,Isaac Penny,Anna Petrovskaya,Mike Pflueger,Ganymed Stanek,David Stavens,Antone Vogt,Sebastian Thrun +24 more
TL;DR: The architecture of Junior, a robotic vehicle capable of navigating urban environments autonomously, successfully finished and won second place in the DARPA Urban Challenge, a robot competition organized by the U.S. Government.
Proceedings ArticleDOI
Discriminative learning of Markov random fields for segmentation of 3D scan data
Dragomir Anguelov,B. Taskarf,Vassil Chatalbashev,Daphne Koller,Divya Gupta,Geremy Heitz,Andrew Y. Ng +6 more
TL;DR: This work addresses the problem of segmenting 3D scan data into objects or object classes by using a recently proposed maximum-margin framework to discriminatively train the model from a set of labeled scans and automatically learn the relative importance of the features for the segmentation task.
Proceedings ArticleDOI
Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion
TL;DR: A fast algorithm able to deal with tremendous amounts of 3D Lidar measurements using a graph-based approach to segment ground and objects from 3D lidar scans using a novel unified, generic criterion based on local convexity measures is presented.
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