An Energy-Efficient Stream-Based FPGA Implementation of Feature Extraction Algorithm for LiDAR Point Clouds With Effective Local-Search

Feature extraction is a fundamental and essential step in light detection and ranging (LiDAR) based simultaneously localization and mapping (SLAM) algorithms. Considering the run-time requirement of feature extraction and the stringent battery constraint in smart vehicles, it is a great challenge to develop fast and highly energy-efficient feature extraction implementation for massive point clouds. Unfortunately, existing implementations not only fail to exploit the available parallelism but also fail to make full use of the local information to optimize the computations. To solve the issue, we propose three novel techniques to achieve a fast and energy-efficient FPGA implementation of the feature extraction algorithm with effective local search. First, we propose a low-complexity projection method and a column-scanning scheduler to organize the irregular and sparse point cloud into a well-organized point cloud matrix. Second, based on the point cloud matrix, we exploit its local information and propose a high-parallel method to detect the coarse-grain feature points. Third, we propose a high-parallel conditional priority queue to progressively and evenly select the fine-grain feature points. Experimental results on the KITTI dataset show that our method implemented on the ZCU104 FPGA board achieves the best accuracy and reaches 584 frames per second (FPS) for the feature extraction of a 64-laser LiDAR’s point cloud. Moreover, our proposal achieves the best energy efficiency, which is on average 11.7 times and 9.0 times higher than the state-of-the-art implementations on the GPU and FPGA platforms, respectively.