Pedestrian Detection Based on Panoramic Depth Map Transformed from 3D-LiDAR Data

Abstract

Object detection is a crucial task of autonomous driving. This paper addresses an effective algorithm for pedestrian detection of the panoramic depth map transformed from the 3D-LiDAR data. Firstly, the 3D point clouds are transformed into panoramic depth maps, and then the panoramic depth maps are enhanced. Secondly, the grounds of the 3D point clouds are removed. The remaining point clouds are clustered, filtered and projected onto the previously generated panoramic depth maps, and new panoramic depth maps are obtained. Finally, the new panoramic depth maps are jointed to generate depth maps with different sizes, which are used as input of the improved PVANET for pedestrian detection. The 2D image of the panoramic depth map applied to the proposed algorithm is transformed from 3D point cloud, effectively containing the panorama of the sensor, and is more suitable for the environment perception of autonomous driving. Compared with the detection algorithm based on RGB images, the proposed algorithm cannot be affected by light, and can maintain the normal average precision of pedestrian detection at night. In order to increase the robustness of detecting small objects like pedestrians, the network structure based on the original PVANET is modified in this paper. A new dataset is built by processing the 3D-LiDAR data and the model trained on the new dataset perform well. The experimental results show that the proposed algorithm achieves high accuracy and robustness in pedestrian detection under different illumination conditions. Furthermore, when trained on the new dataset, the model exhibits average precision improvements of 2.8–5.1 % over the original PVANET, making it more suitable for autonomous driving applications.

A Review on Autonomous Vehicles: Progress, Methods and Challenges

“…Blurry weather conditions limit the preexisting pedestrian detection methods. These reduce the perceptibility and cause hazy outlines in the images taken by the cameras [21]. A predominant challenge faced by these methods is detecting pedestrians in misty weather.…”

“…This has been tested to be faster than the original PVANET and RCNN models [21]. A cheaper algorithm is proposed by [22] based on 2D LiDAR and monocular images-PPLP Net.…”

Segmentation of Facial Bones from Skull Point Clouds Based on Smoothed Deviation Angle

“…Point clouds have the advantage of being able to accurately represent 3-dimensional objects [8]. With this advantage, point clouds have been widely used to represent indoor [9] and outdoor scene [10], [11], [12]. Point clouds can represent tables, chairs, cars, motorcycles [13], [14], trees [15], buildings [16], and roads [17].…”

Method of Using RealSense Camera to Estimate the Depth Map of Any Monocular Camera

“…is ensures that the depth image is obtained independently of the color image. In recent years, active depth map acquisition methods mainly include TOF (Time of Flight) [7], structured light and Kinect [8,9], lidar [10,11], and so on. e principle of the TOF camera [7] to obtain a depth image is as follows: by transmitting continuous near-infrared pulses to the target scene, the light pulses reflected back from the object are received by the sensor.…”

“…is method can also obtain the three-dimensional information of the target with high accuracy and speed, but the effective range is small, the depth value is missing, and the edge of the depth image does not correspond to the edge of the color image and has some noise. e depth information acquisition principle of lidar [10,11] is that laser is fired into space at a certain time interval, and the signal of each scanning point is recorded from the lidar to the objects in the measured scene, as well as the interval time between the signal reflected to the lidar after the object, so as to calculate the distance between the surface of the object and the lidar. Because of its wide ranging range and high measurement accuracy, lidar is widely used in artificial intelligence systems of outdoor three-dimensional space perception, such as obstacle avoidance navigation of autonomous vehicles, threedimensional scene reconstruction, and other applications.…”