DESIGN AND DEVELOPMENT OF A COST-EFFECTIVE LIDAR SYSTEM FOR TRANSPORTATION

摘要

Light Imaging Detection and Ranging (LIDAR) cameras and Light Detecting and Ranging (LiDAR) rangefinders were initially implemented in the 1960s as a higher-resolution and increased capability alternative to radar. Since then. LIDAR and LiDAR (hereto called lidar) have expanded into applications in aerial geographical surveying and collision-detection systems for autonomous vehicles. Current commercial systems are relatively expensive and potentially oversized for noncommercial applications. Consequently, this deters their use on consumer products like bicycles, where lidar systems can enable safety advancements that are necessary to counter the rising numbers of hazards affecting riders. In addition, widespread usage of inexpensive lidar systems can facilitate a more complete picture of our transportation infrastructure by delivering information (e.g., pavement quality) suited for U.S. Department of Transportation Highway Performance Monitoring System (HPMS) reports. This will aid in the creation of a safer infrastructure by highlighting critical areas in need of improvement and repair. As a result, this effort outlines the development of a compact and cost-effective lidar system. The constructed system includes the ability to generate a static image by collecting several hundred thousand distance signals measured by a lidar rangefinder. Since the rangefinder has no self-contained rotation or translation systems, an Arduino Mega 2560 v3 microcontroller operates a pair of stepper motors that adjusts its azimuthal angle and pitch. Coalescing these signals into an ASCII text file for viewing in MATLAB results in a reasonably accurate picture of the surroundings. While the current system takes 1-2 hours to complete a full sweep, it has the potential to provide sufficient accuracy for HPMS reports at a moderate expenditure: the entire system costs less than $300. Finally, upgrading to a more powerful microprocessor, implementing slip rings for enhanced electrical connectivity, and refining the code by including interpolation between points will enable faster point cloud generation while still maintaining an inexpensive device.