A new geometric 3D LiDAR feature for model creation and classification of moving objects

Kusenbach, Michael; Himmelsbach, Michael; Wuensche, Hans-Joachim

doi:10.1109/ivs.2016.7535397

Cited by 19 publications

(10 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The general aim is to optimize the car construction, while preserving the greatest level of protection for vehicle occupants and other traffic participants. These efforts are augmented by various systems of machine vision and remote vehicle detection [1][2][3][4][5][6], or autonomous driving systems using data from precise GPS receivers, LIDAR systems or camera clusters installed in the vehicle [7][8][9][10][11][12]. Novel construction materials are being engineered, such as aluminium alloys, carbon fibre-reinforced composites and new plastics.…”

Section: Introductionmentioning

confidence: 99%

Light Electric Vehicle Powertrain Analysis

Łebkowski

2017

SJSUT.ST

View full text Add to dashboard Cite

Andrzej ŁEBKOWSKI 1 LIGHT ELECTRIC VEHICLE POWERTRAIN ANALYSISSummary. This paper describes the structure of a light electric vehicle known as the Mia Electric vehicle. The vehicle parameters and exploitation properties are presented, while the advantages and disadvantages of the vehicle's technical solutions are discussed, along with possible ideas for their improvement. Vehicle test results on a roller dyno and under actual driving conditions are presented. The data recorded during tests form the basis of an analysis of the vehicle powertrain, whose findings are described in the summary along with testing conclusions.

show abstract

Section: Introductionmentioning

confidence: 99%

Light Electric Vehicle Powertrain Analysis

Łebkowski

2017

SJSUT.ST

View full text Add to dashboard Cite

show abstract

“…collision avoidance, emergency warning) and efficient route planning. [12]. Combination of RGB image and point cloud data has also been studied in some articles [18] [19].…”

Section: List Of Tablesmentioning

confidence: 99%

“…Most of these existing works have shown good performance on clean and complete datasets. For clustering and segmentation most of the existing works (e.g., [11], [31], [12], [32]) focused on the use of the geometric features/clues. When the objective is to separate moving objects from still background (vehicle environment) for scene analysis especially for the purpose of detecting irregular events, we argue it is more effective to exploit motion-related information (e.g., trajectory, speed and acceleration).…”

Section: Literature Surveymentioning

confidence: 99%

Multi-Sensor Data Fusion for Robust Environment Reconstruction in Autonomous Vehicle Applications

Rubaiyat¹

View full text Add to dashboard Cite

In autonomous vehicle systems, understanding the surrounding environment is mandatory for an intelligent vehicle to make every decision of movement on the road. Knowledge about the neighboring environment enables the vehicle to detect moving objects, especially irregular events such as jaywalking, sudden lane change of the vehicle etc. to avoid collision. This local situation awareness mostly depends on the advanced sensors (e.g. camera, LIDAR, RADAR) added to the vehicle. The main focus of this work is to formulate a problem of reconstructing the vehicle environment using point cloud data from the LIDAR and RGB color images from the camera. Based on a widely used point cloud registration tool such as iterated closest point (ICP), an expectationmaximization (EM)-ICP technique has been proposed to automatically mosaic multiple point cloud sets into a larger one. Motion trajectories of the moving objects are analyzed to address the issue of irregularity detection. Another contribution of this work is the utilization of fusion of color information (from RGB color images captured by the camera) with the three-dimensional point cloud data for better representation of the environment. For better understanding of the surrounding environment, histogram of oriented gradient (HOG) based techniques are exploited to detect pedestrians and vehicles. Using both camera and LIDAR, an autonomous vehicle can gather information and reconstruct the map of the surrounding environment up to a certain distance. Capability of communicating and cooperating among vehicles can improve the automated driving decisions by providing extended and more precise view of the surroundings. In this work, a transmission power control algorithm is studied along with the adaptive content control algorithm to achieve a more accurate map of the vehicle environment. To exchange the local sensor data among the vehicles, an adaptive communication scheme is proposed that controls the lengths and the contents of the messages depending on the load of the communication channel. The exchange of this information can extend the tracking region of a vehicle beyond the area sensed by its own sensors. In this experiment, a combined effect of power control, and message length and content control algorithm is exploited to improve the map's accuracy of the surroundings in a cooperative automated vehicle system. I would also like to convey my heartfelt thanks to Dr. Xin Li for his continuous guidance. His advice and innovative ideas helped me to successfully complete my thesis. I greatly appreciate my thesis committee members, Dr. Parviz Famouri and Dr. Muhammad A. Choudhry, for their time and support. I wish to acknowledge the Lane Department of Computer Science and Electrical Engineering to give me the opportunity of pursuing my master's degree in such an academically vibrant environment. I would like to thank my family for supporting me throughout my studies. I am also thankful to the members of my research group for their help during my research at WVU.

show abstract

“…Just like image data, clustering and segmentation of point cloud data are handy tools supporting other high-level tasks such as classification and recognition. Existing works (e.g., [6], [17], [7], [18]) have mostly focused on the use of geometric features/clues for clustering and segmentation. With the separation of moving objects from still background (vehicle environment), we argue it is more fruitful to exploit motion-related information (e.g., trajectory, speed and acceleration).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Various sensors including visible-spectrum cameras, radars and LIDARs have provided a rich collection of data that can be exploited by intelligent vehicles; though the cost and accuracy of different sensors (e.g.,camera vs. LIDAR) could vary [1], [2]. Among those sensors, LIDAR-based data have received increasingly more attention recently due to rapid advances in both hardware (e.g., from Velodyne HDL-16e, 32e to 64e) and software (i.e., point cloud registration [3], [4], [5], segmentation [6] and classification [7]). Under the context of intelligent vehicles, LIDARbased approaches have been studied for the detection/tracking of pedestrian [8], [9] curb [10], [11] and vehicle [12], [7].…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

Point Cloud Processing Algorithms for Environment Understanding in Intelligent Vehicle Applications

Sidiya¹

View full text Add to dashboard Cite

A new geometric 3D LiDAR feature for model creation and classification of moving objects

Cited by 19 publications

References 20 publications

Light Electric Vehicle Powertrain Analysis

Light Electric Vehicle Powertrain Analysis

Multi-Sensor Data Fusion for Robust Environment Reconstruction in Autonomous Vehicle Applications

Point Cloud Processing Algorithms for Environment Understanding in Intelligent Vehicle Applications

Contact Info

Product

Resources

About