Object Recognition from 3D Point Cloud on Resource-Constrained Edge Device

This paper presents the design and development of a lightweight, portable 3D spatial sensing device equipped with a compact LiDAR-type sensor. The device provides a 3D point cloud representation of the surrounding environment as captured by the sensor. Based on the acquired 3D point cloud, we propose a real-time object recognition method. The technical challenge is how to process the 3D point data in real-time while pursuing the best trade-off between the processing overhead and recognition accuracy. To answer the question, we leverage the Fisher Vector to extract spatio-temporal features of different objects enabling efficient classification of these objects using the Support Vector Machine approach. The experimental results show that the proposed method achieves mean Average Precision of 0.961. The processing speed on the device was 59.3 frames/second, indicating that object detection can be done in real-time on the device.