Radar Self-Following Shopping Cart Based on Multi-Sensor Fusion

Abstract

Long queues of people waiting for a settlement in global shopping supermarkets are bustling with traffic. In order to further improve operating efficiency and reduce the cost of manual operation of the supermarket, this article proposes a multi-sensor integration shopping cart system, which mainly includes image recognition, radar self-following technology and road patrol. The improved SIFT algorithm effectively identifies a variety of commodities. At the same time, relying on the public cloud platform, discount information is recommended through WeChat programs, and the development of member users login interface. After comprehensive calculation, the total price is displayed by the EAIDK-610 user interface edited by Tkinter to complete the checkout. The LD-14 radar used can realize the functions of autonomous follow-up and pedestrian obstacles. When a sensor in the system fails or the detection deviation is too large, the multi-sensor fusion technology can ensure the normal operation of the system due to a certain degree of redundant. Improve the credibility and effectiveness of the data as a whole. This not only solves the problem of queuing and checkout in the supermarket, but also helps brand merchants to achieve precise marketing and scenario access. Finally, the use of computer vision technology and various embedded hardware devices to build a radar self-following the hardware prototype of the shopping cart for experimental verification. The average accuracy of APs in the system is as high as 98.31%and 97.28%, respectively.

Keywords

• Radar self-following
• multi-sensor
• public cloud platform
• image recognition
• road patrol
• computer vision