Authors:
Mircea Paul Muresan
;
Robert Schlanger
;
Radu Danescu
and
Sergiu Nedevschi
Affiliation:
Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
Keyword(s):
3D Object Detection, Road Surface Estimation, Autonomous Driving, CUDA, Parallel Programming, LiDAR Point Clouds.
Abstract:
In contrast to image-based detection, objects detected from 3D LiDAR data can be localized easier and their shapes are easier identified by using depth information. However, the 3D LiDAR object detection task is more difficult due to factors such as the sparsity of the point clouds and highly variable point density. State-of-the-art learning approaches can offer good results; however, they are limited by the data from the training set. Simple models work only in some environmental conditions, or with specific object classes, while more complex models require high running time, increased computing resources and are unsuitable for real-time applications that have multiple other processing modules. This paper presents a GPU-based approach for detecting the road surface and objects from 3D LiDAR data in real-time. We first present a parallel working architecture for processing 3D points. We then describe a novel road surface estimation approach, useful in separating the ground and object
points. Finally, an original object clustering algorithm that is based on pillars is presented. The proposed solution has been evaluated using the KITTI dataset and has also been tested in different environments using different LiDAR sensors and computing platforms to verify its robustness.
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