Visual Landmarks Map-based Spatial Recognition using Monocular Camera

학위논문(석사)--AI융합네트워크학과,2023. 8

I. Introduction 1 <br>II. Background 4 <br> 2.1 Spatial Recognition 4 <br> 2.1.1 Difference Between Spatial Recognition and Indoor Positioning 4 <br> 2.1.2 Spatial Recognition Research Focus 4 <br> 2.2 Landmark Map 5 <br> 2.3 Object Detection 6 <br> 2.3.1 Object Detection Models 7 <br> 2.3.2 Yolov7 Overview 8 <br>III. Visual Landmark Map Making and Spatial Recognition 10 <br> 3.1 System Structure 10 <br> 3.2 Make Landmarks 12 <br> 3.2.1 Data Collection 12 <br> 3.2.2 Data Sort 13 <br> 3.2.3 Noise Types 15 <br> 3.2.4 Noise Remove 16 <br> 3.2.5 Data Composition of Each Object 18 <br> 3.2.6 Make Landmark Topological Relationship 19 <br> 3.3 Visual Landmark Map 23 <br> 3.4 Spatial Recognition 23 <br>IV. Experiment 27 <br> 4.1 Experiment Set 27 <br> 4.2 Experiment Result 27 <br>V.Conclusions 30 <br>References 31

Master

With the increasing popularity of computer vision, in the context of the development of Computer Vision(CV) and robot technology today, computer vision has more applications in many fields, such as in space recognition technology, accurate space recognition is important for robot navigation、positioning、route planning. However, traditional computer vision-based spatial recognition relies heavily on multiple cameras or depth sensors, which is costly and complex. Therefore, this paper proposes a visual landmarks map-based spatial recognition using monocular camera which extracts the spatial information of objects in space through video, and perform operations such as noise reduction and de-duplication filtering on the data to improve the matching rate of landmarks and spaces and the accuracy of space recognition. In order to verify the performance of the proposed algorithm, a series of tests are carried out. The results show that the map landmarks can achieve a high degree of similarity to the real situation in real space, and compared with previous algorithms, the method improves the success accuracy to 87.5%.