Target Position and Speed Estimation Using LiDAR

被引：5

作者：

Dayangac, Enes ^{[1
]}

Baumann, Florian ^{[1
]}

Aulinas, Josep ^{[1
]}

Zobel, Matthias ^{[1
]}

机构：

[1] ADASENS Automot GmbH, Oberhof 13, D-88138 Weissensberg, Germany

来源：

IMAGE ANALYSIS AND RECOGNITION (ICIAR 2016) | 2016年 / 9730卷

关键词：

LiDAR; Velodyne; Kalman filter; Multi-hypotheses; 3D point cloud; Sensor fusion; Distance; Speed estimation;

D O I：

10.1007/978-3-319-41501-7_53

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, an efficient and reliable framework to estimate the position and speed of moving vehicles is proposed. The method fuses LiDAR data with image based object detection algorithm output. LiDAR sensors deliver 3D point clouds with a positioning accuracy of up to two centimeters. 2D object data leads to a significant reduction of the search space. Outliers removal techniques are applied to the reduced 3D point cloud for a more reliable representation of the data. Furthermore, a multi-hypothesis Kalman filter is implemented to determine the target object's speed. The accuracy of the position and velocity estimation is verified through real data and simulation. Additionally, the proposed framework is real-time capable and suitable for embedded-vision related applications.

引用

页码：470 / 477

页数：8

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