We implement our methodology on a Turtlebot 2 robot personalized with additional aluminium rods to connect a 15-inch screen to display messages to the non-compliant pedestrians. We limit the robot’s maximum linear and angular velocities to zero.sixty f
We implement our methodology on a Turtlebot 2 robot personalized with additional aluminium rods to connect a 15-inch screen to display messages to the non-compliant pedestrians. We limit the robot’s maximum linear and angular velocities to zero.sixty five m/s and 1 rad/s respectively. The pedestrian detection and monitoring algorithm is executed on a laptop with an Intel i9 8th generation CPU and an Nvidia RTX2080 GPU mounted on the robot. We use an Intel RealSense (with 70° FOV) RGB-D camera to sense pedestrians and a Hokuyo 2-D lidar (240° FOV) to sense other environmental obstacles. To emulate a CCTV camera setup, we used an RGB digicam with a 1080p resolution mounted at an elevation of 4 meters, and a sensing area of 4m × 4m.
Lidar And Level Cloud Processing
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S1 Video Video With Real
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