The Application of SLAM in Life and Scientific Research

Abstract

This article mainly focuses on the application of SLAM in technology and provides a literature review on the following aspects: 1) The application of SLAM in automatic parking of automobiles. This includes using AVP-SLAM for high-precision automatic parking in outdoor parking lots using convolutional neural networks, and using MOFISLM for high-precision automatic parking when indoor GPS signals are poor. 2) The application of SLAM in robotics. It includes three aspects. The first is using the GMapping algorithm to construct and locate maps with high accuracy, low computational complexity, and high robustness, enabling robots to plan the shortest path in unknown environments. The second is to use ORB-SLAM2, which can handle more complex situations, to navigate medical institution robots to improve hospital efficiency and reduce disease transmission rates. The third is for robots that perform tasks in complex and unknown terrains. The improved version of LAMP1.0, LAMP2.0, which is suitable for complex underground environments, can achieve multi robot collaborative cooperation with high accuracy and robustness. 3) Application of SLAM in the field of unmanned aerial vehicles. This article mainly elaborates on four aspects. The first is to use visual SLAM to achieve collaborative work between ground robots and drones, and to utilize the high camera advantage of drones to quickly build maps for ground robots, improve accuracy, and save time. The second is to utilize the application of VSLAM on unmanned aerial vehicles to contribute to agriculture. The third is ORB-SLAM3 to provide rapid and high-precision landing on deck for unmanned aerial vehicles in maritime missions. The fourth is using SLAM to assist unmanned aerial vehicles in landing in emergency or position situations (such, when GPS signals are lost).