Real-Time Object Recognition from Streaming LiDAR Point Cloud Data

Sambasiva Rao Gangineni, Harshad Reddy Nalla, Saeed Fathollahzadeh, Kia Teymourian

Abstract: In many robotic applications, LiDAR (Light Detection and Ranging) scanner is used to gather data about the environment. Applications like autonomous vehicles require real-time processing of LiDAR point cloud data with high accuracy. We describe in this paper, our implementation for DEBS 2019 Grand Challenge for an object recognition system from high-speed LiDAR data stream. Our system includes a data processing pipeline with 3 main stages, 1. LiDAR data filtering 2. Object segmentation and noise reduction 3. Multi-class object classification using Convolutional Neural Network (CNN). Our evaluation shows that we can classify objects with high accuracy using the point cloud data and neural network. However, we observed that the classification may fail if the object segmentation is not separating objects correctly in different segments especially when the objects are largely covering each other. We proposed a pre-processing approach for object segmentation based on separating LiDAR data into multiple area sectors before segmenting the objects.