Graph-based 2D road representation of 3D point clouds for intelligent vehicles

Guo, Chunzhao; Sato, Wataru; Han, Li; Mita, Seiichi; McAllester, David

doi:10.1109/ivs.2011.5940502

Cited by 37 publications

(24 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach keeps the full 3D information delivered by the sensor unlike many other popular approaches. • MRF -based methods: Several MRF-based road detection methods in [8], [9] have the potential for ground segmentation. These methods use the gradient cues of the road geometry to construct MRF and implement a belief propagation (BP) algorithm to classify the surrounding into different regions.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Ground estimation and point cloud segmentation using SpatioTemporal Conditional Random Field

Rummelhard

Paigwar

Nègre

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

Whether it be to feed data for an object detectionand-tracking system or to generate proper occupancy grids, 3D point cloud extraction of the ground and data classification are critical processing tasks, on their efficiency can drastically depend the whole perception chain. Flat-ground assumption or form recognition in point clouds can either lead to systematic error, or massive calculations. This paper describes an adaptive method for ground labeling in 3D Point clouds, based on a local ground elevation estimation. The system proposes to model the ground as a Spatio-Temporal Conditional Random Field (STCRF). Spatial and temporal dependencies within the segmentation process are unified by a dynamic probabilistic framework based on the conditional random field (CRF). Ground elevation parameters are estimated in parallel in each node, using an interconnected Expectation Maximization (EM) algorithm variant. The approach, designed to target high-speed vehicle constraints and performs efficiently with highly-dense (Velodyne-64) and sparser (Ibeo-Lux) 3D point clouds, has been implemented and deployed on experimental vehicle and platforms, and are currently tested on embedded systems (Nvidia Jetson TX1, TK1). The experiments on real road data, in various situations (city, countryside, mountain roads,...), show promising results.

show abstract

Section: Introduction and Related Workmentioning

confidence: 99%

Ground estimation and point cloud segmentation using SpatioTemporal Conditional Random Field

Rummelhard

Paigwar

Nègre

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

show abstract

“…Efficiency is improved in his approach, splitting the problem into two simpler subproblems of complexity reduction: local ground plane is estimated by the two-dimension labeling components [7]. Guo et al used the gradient signaling of the road geometry to construct a Markov random field (MRF) and implement an effective belief-propagation (BP) algorithm to put the road environment into four categories: the reachable area, the driving area, the obstacle region, and the unknown region [8]. Although the above detection method based on the grid is relatively stable, it has inherent defects.…”

Section: Introductionmentioning

confidence: 99%

Obstacle Detection in Hybrid Cross-Country Environment Based on Markov Random Field for Unmanned Ground Vehicle

Ding

Zhao

Guo

et al. 2015

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

In order to detect the obstacle from the large amount of 3D LIDAR data in hybrid cross-country environment for unmanned ground vehicle, a new graph approach based on Markov random field was presented. Firstly, the preprocessing method based on the maximum blurred line is applied to segment the projection of every laser scan line inx-yplane. Then, based onK-means clustering algorithm, the same properties of the line are combined. Secondly, line segment nodes are precisely positioned by using corner detection method, and the next step is to take advantage of line segment nodes to build an undirected graph for Markov random field. Lastly, the energy function is calculated by means of analyzing line segment features and solved by graph cut. Two types of line mark are finally classified into two categories: ground and obstacle. Experiments prove the feasibility of the approach and show that it has better performance and runs in real time.

show abstract

“…In this project, reliable perception of the environment is the first and most important step for autonomous driving. Recently, 3D-Lidar sensors have been widely used in this area because they provide rich and accurate data of spatial information around the vehicle [1,3,4,7,10]. In this paper, we propose an effective MTT algorithm using a Velodyne 3D HDL-64 Lidar sensor.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, ground classification and ground removal has to be executed before any following processing steps. The typical algorithms for that are summarized in [4] and limitations of each algorithm are discussed as well. Another approach for ground classification is presented in [10] which uses a 2.5D occupancy grid.…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Tracking using a 3D-Lidar sensor for autonomous vehicles

Choi

Ulbrich

Lichte

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

View full text Add to dashboard Cite

Environmental perception is a prerequisite for autonomous driving and also a challenging task particularly in cluttered dynamic environments such as complex urban situations. In this paper, we present a robust algorithm for Multi-Target Tracking (MTT) using a Velodyne 3D HDL-64 Lidar sensor. The main contribution of this paper is a practical framework for selecting and representing useful information from the sensor raw data. Since the sensor produces a huge amount of data, a perception algorithm cannot be carried out in real-time without simplifying the sensor information. Unlike prior works, we introduce hybrid ground classification and the Region of Interest (ROI) identification method in order to filter out the amount of unwanted raw data for the actual tracking. And the environment is also abstracted based on an occupancy grid map. Moreover, we introduce feature based object geometry for precise estimation of the system state. In contrast to prior approaches, which use object geometry for the classification, we use it in order to compensate the unintended dynamics caused by shape change or occlusion. Our proposed MTT algorithm is able to run in real-time with an average processing time of 20ms. We evaluate it using our experimental vehicle "Leonie" in complex urban scenarios.

show abstract

Graph-based 2D road representation of 3D point clouds for intelligent vehicles

Cited by 37 publications

References 12 publications

Ground estimation and point cloud segmentation using SpatioTemporal Conditional Random Field

Ground estimation and point cloud segmentation using SpatioTemporal Conditional Random Field

Obstacle Detection in Hybrid Cross-Country Environment Based on Markov Random Field for Unmanned Ground Vehicle

Multi-Target Tracking using a 3D-Lidar sensor for autonomous vehicles

Contact Info

Product

Resources

About