Robust Localization for Intelligent Vehicles Based on Pole-Like Features Using the Point Cloud

Lidar is a key sensor of autonomous driving systems, but the spatial distribution of its point cloud is uneven because of its scanning mechanism, which greatly degrades the clustering performance of the traditional density-based spatial clustering of application with noise (DSC). Considering the outline feature of detected objects for intelligent vehicles, a DSC-based adaptive clustering method (DAC) is proposed with the adoption of an elliptic neighborhood, which is designed according to the distribution properties of the point cloud. The parameters of the ellipse are adaptively adjusted with the location of the sample point to deal with the uniformity of points in different ranges. Furthermore, the dependence among different parameters of DAC is analyzed, and the parameters are numerically optimized with the KITTI dataset by considering comprehensive performance. To verify the effectiveness, a comparative experiment was conducted with a vehicle equipped with three IBEO LUX8 lidars on campus, and the results show that compared with DSC using a circular neighborhood, DAC has a better clustering performance and can notably reduce the rate of over-segmentation and under-segmentation.

Download Full-text

Point Cloud Registration Based on Direct Deep Features With Applications in Intelligent Vehicles

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2021.3123619 ◽

2021 ◽

pp. 1-12

Author(s):

Liang Li ◽

Ming Yang

Keyword(s):

Point Cloud ◽

Intelligent Vehicles ◽

Point Cloud Registration

Download Full-text

Deep learning-based dynamic object classification using LiDAR point cloud augmented by layer-based accumulation for intelligent vehicles

Expert Systems with Applications ◽

10.1016/j.eswa.2020.113861 ◽

2020 ◽

pp. 113861

Author(s):

Kyungpyo Kim ◽

Chansoo Kim ◽

Chulhoon Jang ◽

Myoungho Sunwoo ◽

Kichun Jo

Keyword(s):

Deep Learning ◽

Point Cloud ◽

Object Classification ◽

Intelligent Vehicles ◽

Dynamic Object

Download Full-text

Rapid Motion Segmentation of LiDAR Point Cloud Based on a Combination of Probabilistic and Evidential Approaches for Intelligent Vehicles

Sensors ◽

10.3390/s19194116 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4116

Author(s):

Kichun Jo ◽

Sumyeong Lee ◽

Chansoo Kim ◽

Myoungho Sunwoo

Keyword(s):

Point Cloud ◽

Motion Segmentation ◽

Evidence Theory ◽

Point Clouds ◽

Intelligent Vehicles ◽

Segmentation Algorithm ◽

Environmental Object ◽

Movement Characteristics ◽

Segmentation Methods ◽

Point Motion

Point clouds from light detecting and ranging (LiDAR) sensors represent increasingly important information for environmental object detection and classification of automated and intelligent vehicles. Objects in the driving environment can be classified as either d y n a m i c or s t a t i c depending on their movement characteristics. A LiDAR point cloud is also segmented into d y n a m i c and s t a t i c points based on the motion properties of the measured objects. The segmented motion information of a point cloud can be useful for various functions in automated and intelligent vehicles. This paper presents a fast motion segmentation algorithm that segments a LiDAR point cloud into d y n a m i c and s t a t i c points in real-time. The segmentation algorithm classifies the motion of the latest point cloud based on the LiDAR’s laser beam characteristics and the geometrical relationship between consecutive LiDAR point clouds. To accurately and reliably estimate the motion state of each LiDAR point considering the measurement uncertainty, both probability theory and evidence theory are employed in the segmentation algorithm. The probabilistic and evidential algorithm segments the point cloud into three classes: d y n a m i c , s t a t i c , and u n k n o w n . Points are placed in the u n k n o w n class when LiDAR point cloud is not sufficient for motion segmentation. The point motion segmentation algorithm was evaluated quantitatively and qualitatively through experimental comparisons with previous motion segmentation methods.

Download Full-text

Registration of GPS and Stereo Vision for Point Cloud Localization in Intelligent Vehicles Using Particle Swarm Optimization

Lecture Notes in Computer Science - Advances in Swarm Intelligence ◽

10.1007/978-3-319-61824-1_23 ◽

2017 ◽

pp. 209-217 ◽

Cited By ~ 3

Author(s):

Vijay John ◽

Yuquan Xu ◽

Seiichi Mita ◽

Qian Long ◽

Zheng Liu

Keyword(s):

Particle Swarm Optimization ◽

Stereo Vision ◽

Point Cloud ◽

Particle Swarm ◽

Intelligent Vehicles ◽

Swarm Optimization

Download Full-text

Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information

Journal of Advanced Transportation ◽

10.1155/2022/4736623 ◽

2022 ◽

Vol 2022 ◽

pp. 1-21

Author(s):

Ruibin Zhang ◽

Yingshi Guo ◽

Yunze Long ◽

Yang Zhou ◽

Chunyan Jiang

Keyword(s):

Point Cloud ◽

Short Term Memory ◽

Time Sequence ◽

Object Motion ◽

Intelligent Vehicles ◽

Prediction Algorithm ◽

Interaction Information ◽

Motion State ◽

State Prediction ◽

Vehicle Motion

A vehicle motion state prediction algorithm integrating point cloud timing multiview features and multitarget interaction information is proposed in this work to effectively predict the motion states of traffic participants around intelligent vehicles in complex scenes. The algorithm analyzes the characteristics of object motion that are affected by the surrounding environment and the interaction of nearby objects and is based on the complex traffic environment perception dual multiline light detection and ranging (LiDAR) technology. The time sequence aerial view map and time sequence front view depth map are obtained using real-time point cloud information perceived by the LiDAR. Time sequence high-level abstract combination features in the multiview scene are then extracted by an improved VGG19 network model and are fused with the potential spatiotemporal interaction of the multitarget operation state data extraction features detected by the laser radar by using a one-dimensional convolution neural network. A temporal feature vector is constructed as the input data of the bidirectional long-term and short-term memory (BiLSTM) network, and the desired input-output mapping relationship is trained to predict the motion state of traffic participants. According to the test results, the proposed BiLSTM model based on point cloud multiview and vehicle interaction information is better than other methods in predicting the state of target vehicles. The results can provide support for the research to evaluate the risk of intelligent vehicle operation environment.

Download Full-text