Sensing Performance Analysis of Mobile Robot Navigation Based on Depth Camera and Lidar

One of the potential implementations of mobile robots in mitigating the risk of communicable diseases is for delivering items and handling materials with no-human contact. In that scenario, mobile robots require the capability of performing simultaneous multi-object detection and distance estimation for dynamic exploration in a real-world environment. Some existing research papers have contributed to the development of object detection or distance estimation, separately. However, there is still a lack of studies on simultaneous multi-object detection and distance estimation for robot navigation, especially in terms of performance analysis of the integrated techniques. One of the most challenging issues of integrating multi-object detection and distance estimation is the validation and evaluation of the type of objects with their relevant estimated distance to the robot. For that reason, this research aims to analyze the mobile robot's sensing performance in integrated simultaneous multi-object detection and distance estimation during navigation scenarios. A depth camera of Intel RealSense D455 and a rotating LIDAR of Slamtec RPLidar A1M8 are utilized in the experiments. The result of experiments shows that the object detection algorithm using Mobilenet SSD V2 can perform an accuracy of 74%, with precision, recall, and F1-Score consecutively achieving 74,54%, 74,00%, and 74,22%. The multi-object distance estimator also performs satisfactory results with a low measurement average error of 10.3 cm for indoor experiments. The integrated multiobject detector and distance estimator system can work in a real-time mode of 30 FPS, thence significantly improving the navigation performance of the mobile robot.