Low-Latency LiDAR Semantic Segmentation

Abstract

Several methods of semantic segmentation using light detection and ranging (LiDAR) sensors have been proposed for the recognition of surrounding objects by autonomous driving cars. LiDAR is a sensor that compensates for the weaknesses of other sensors, such as cameras or radar systems, and semantic segmentation assigns a class label to each point in the LiDAR point cloud. Recently, real-time semantic segmentation methods that are capable of processing LiDAR point clouds at frame rates have been proposed. Real-time semantic segmentation is essential for the autonomous driving system because it can output class labels for LiDAR point clouds at high speeds. However, this segmentation method suffers from a delay equal to processing time. To address this challenge, we propose a novel method that combines SalsaNext [1], a method of real-time LiDAR semantic segmentation, and semantic forecasting, which predicts the results of future semantic segmentation. We quantitatively evaluate our method using the Semantic-KITTI dataset, which comprises point cloud data acquired from the LiDAR sensor in the real world, and compare the latency and accuracy of our method with other semantic segmentation methods. Consequently, our method is found to be capable of operating in real-time and with low-latency, and it can achieve a performance similar to that of previously reported real-time semantic segmentation methods.