FMCW LiDAR System to Reduce Hardware Complexity and Post-Processing Techniques to Improve Distance Resolution

On the use of field programmable gate arrays in light detection and ranging systems

This work aims to provide details on the latest technological developments regarding LiDAR (Light Imaging Detection And Ranging) systems, with particular reference to the techniques, architectures, and methodologies partially or entirely implemented by means of the FPGA (Field Programmable Gate Array) environment. Currently, LiDAR technology is considered of great interest as it is widely employed in a variety of application fields, such as automotive, seismology, archaeology, metrology, and military. For this reason, the required performances are gradually increasing, which leads to complex and stringent solutions. The growth in LiDAR systems' complexity suggests the use of high-end general-purpose computing units such as central processing units to perform very complex tasks and FPGAs to perform multiple tasks in real-time through the implementation of dedicated computational blocks. The latter, in recent architectures, are therefore used for the execution of specific tasks that require high computational speed and system flexibility. This paper reports some case studies recently applied in the LiDAR field, with the aim of illustrating the role of FPGA technology and its benefits.

Detection and Tracking of Pedestrians Using Doppler LiDAR

Pedestrian detection and tracking is necessary for autonomous vehicles and traffic management. This paper presents a novel solution to pedestrian detection and tracking for urban scenarios based on Doppler LiDAR that records both the position and velocity of the targets. The workflow consists of two stages. In the detection stage, the input point cloud is first segmented to form clusters, frame by frame. A subsequent multiple pedestrian separation process is introduced to further segment pedestrians close to each other. While a simple speed classifier is capable of extracting most of the moving pedestrians, a supervised machine learning-based classifier is adopted to detect pedestrians with insignificant radial velocity. In the tracking stage, the pedestrian’s state is estimated by a Kalman filter, which uses the speed information to estimate the pedestrian’s dynamics. Based on the similarity between the predicted and detected states of pedestrians, a greedy algorithm is adopted to associate the trajectories with the detection results. The presented detection and tracking methods are tested on two data sets collected in San Francisco, California by a mobile Doppler LiDAR system. The results of the pedestrian detection demonstrate that the proposed two-step classifier can improve the detection performance, particularly for detecting pedestrians far from the sensor. For both data sets, the use of Doppler speed information improves the F1-score and the recall by 15% to 20%. The subsequent tracking from the Kalman filter can achieve 83.9–55.3% for the multiple object tracking accuracy (MOTA), where the contribution of the speed measurements is secondary and insignificant.

Concurrent Firing Light Detection and Ranging System for Autonomous Vehicles

We proposed a light detection and ranging (LIDAR) system that changes the measurement strategy from a LIDAR system of sequential emission and measuring method to a concurrent firing measuring method. The proposed LIDAR was a 3D scanning LIDAR method that consisted of 128 output channels in one vertical line in the measurement direction and concurrently measured the distance for each of these 128 channels. The scanning LIDAR emitted 128 laser pulse streams encoded by carrier-hopping prime code (CHPC) technology with identification and checksum. When the reflected pulse stream was received and demodulated, the emission channel could be recognized. This information could be used to estimate the time when the laser pulse stream was emitted and calculate the distance to the object reflecting the laser. By using the identification of the received reflected wave, even if several positions were measured at the same time, the measurement position could be recognized after the reception. Extensive simulations indicated that the proposed LIDAR could provide autonomous vehicles or autonomous walking robots with good distance images to recognize the environment ahead.