# Distance Assessment by Object Detection--For Visually Impaired Assistive Mechatronic System
## 3 Materials and Methods
### RPLIDAR and HQ Camera Subsystem  
To perform the experiments of the identification and recognition of objects/obstacles, we used the following devices:  
1. Raspberry Pi HQ camera 12.30 MP An independent high-fidelity visual peripheral mode in low light HQ camera light conditions, Raspberry Pi brand, with the interconnected structure of both hardware and software for image capture. 2. 6 mm and 3 MP fisheye lens for Raspberry Pi HQ camera An interchangeable lens mount with CS mount, compatible with the 12.30 MP Raspberry Pi HQ camera peripheral module, with a wide angle of the real image, also called \"fisheye\". 3. 16 mm and 10 MP telephoto lens for Raspberry Pi HQ camera An interchangeable telephoto lens with a type C mount, compatible with the 12.30 MP Raspberry Pi HQ camera peripheral module, with a narrow viewing angle. 4. RPLIDAR A3 Sensor In order to measure the distances between the designed mobile mechatronic system and the objects in the work environment, we used the omnidirectional RPLIDAR A3 sensor, a product of Shanghai Slamtec Co., which works on the principle of triangulation. It is equipped with a rotating laser beam of 16,000 rot/s, allows contour mapping of the surrounding area, positioning, and autonomous navigation and avoids obstacles. The technology incorporated into this sensor has a good sensitivity in the case of the existence of black and white bodies. 5. NVIDIA Jetson Nano P3449_B01 The Jetson Nano Model P3449_B01 is a board with optimized power and low power consumption, provided by NVIDIA Corporation, designed to run at high speeds, and simultaneously gathers much more data related to the detection and object recognition. The research on the detection and recognition of objects using the materials presented above is carried out through the prototype of an original mechatronic system (see Figure 7a). The Raspberry Pi HQ IMX477 camera with different lenses (on the left side of the image is the 16 mm telephoto lens, and on the right side is the 6 mm \"fisheye\" lens). The Jetson Nano board model P3449_B01 and the RPLIDAR A3 laser scanner are installed on the mobile robotic platform. Figure 6: Feature detection and object distance localization on calibration curve. (**a**) 20 features detected; (**b**) localization on calibration curve for an object at a 200 mm distance. The technique adopted for the experiments in the real environment is evidenced in Figure 7 and is explained as follows:  
1. The distances between the camera and the test object have values of 0.50 m, 1.00 m, and 1.50 m, taking into account the constructive restrictions of the robotic arm. The databases used are neural networks with convolutional architecture and increased accuracy, reduced in terms of capacity, respectively GoogLeNet of 54 MB and ResNet-50 of 103 MB;
2. The cameras are used alternately so as not to alter the positions and technical characteristics of the visual perception subsystem;
3. The software used for visualizing the environment is RViz, installed under the operating system for robots, ROS Melodic (see Figure 8b). In order to work on the NVIDIA Jetson Nano platform and to be able to collect data from the HQ camera, as well as from the RPLIDAR scanner, according to Figure 7b, this software benefits from a suite of software resources, some provided by the hardware manufacturer, and other open-source software optimized for this board. NVIDIA provides users with hardware components and software packages that are not always upgraded. In this case, the NVIDIA JetPack 4.4 SDK (Software Development Kit) was used. It was followed by the configuration of the operating system on the nano board for the robotic system to process graphic/video information, to detect objects, to calculate the distance to them, etc. In addition, software packages are required for AI, Computer Vision, image processing, interfaces, multimedia processing, graphics, etc. Figure 8a shows the basic software components;
4.