TY - GEN
T1 - A Lightweight RGB-D Image-Based 3D Object Detector for Mobile Robots
AU - Qi, Zhangshuo
AU - Luo, Zhen
AU - Ma, Junyi
AU - Xiong, Guangming
N1 - Publisher Copyright:
© Beijing HIWING Scientific and Technological Information Institute 2024.
PY - 2024
Y1 - 2024
N2 - 3D object detection enables mobile robots to operate effectively in real-world scenarios. To enhance the performance of 3D object detection without compromising inference speed, we present a novel 3D object detection method that leverages RGB-D images. Our proposed approach involves multiple stages. Firstly, we extract 2D bounding boxes from images and corresponding point clouds within frustums. Subsequently, the point clouds within the frustums are processed by a devised proposal generation module, generating bounding box proposals. Additionally, we leverage a refinement network to enhance the accuracy of 3D bounding box estimation. Our object detection framework requires minimal prior information and achieves a nearly real-time performance of 20 FPS while maintaining high accuracy. To validate the effectiveness of our proposed method, we conduct experiments on the SUN RGB-D 3D detection benchmarks. The results demonstrate that our approach outperforms other baseline methods regarding object detection accuracy, spatial complexity, computational cost, and inference speed.
AB - 3D object detection enables mobile robots to operate effectively in real-world scenarios. To enhance the performance of 3D object detection without compromising inference speed, we present a novel 3D object detection method that leverages RGB-D images. Our proposed approach involves multiple stages. Firstly, we extract 2D bounding boxes from images and corresponding point clouds within frustums. Subsequently, the point clouds within the frustums are processed by a devised proposal generation module, generating bounding box proposals. Additionally, we leverage a refinement network to enhance the accuracy of 3D bounding box estimation. Our object detection framework requires minimal prior information and achieves a nearly real-time performance of 20 FPS while maintaining high accuracy. To validate the effectiveness of our proposed method, we conduct experiments on the SUN RGB-D 3D detection benchmarks. The results demonstrate that our approach outperforms other baseline methods regarding object detection accuracy, spatial complexity, computational cost, and inference speed.
KW - 3D Object Detection
KW - Environment Perception
KW - Mobile Robots
UR - http://www.scopus.com/inward/record.url?scp=85192920511&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-1099-7_5
DO - 10.1007/978-981-97-1099-7_5
M3 - Conference contribution
AN - SCOPUS:85192920511
SN - 9789819710980
T3 - Lecture Notes in Electrical Engineering
SP - 45
EP - 55
BT - Proceedings of 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 - Volume 6
A2 - Qu, Yi
A2 - Gu, Mancang
A2 - Niu, Yifeng
A2 - Fu, Wenxing
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023
Y2 - 9 September 2023 through 11 September 2023
ER -