실내 서비스로봇을 위한 전방 단안카메라 기반 SLAM 시스템

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 조동일.This dissertation presents a new forward-viewing monocular vision-based simultaneous localization and mapping (SLAM) method. The method is developed to be applicable in real-time on a low-cost embedded system for indoor service robots. The developed system utilizes a cost-effective mono-camera as a primary sensor and robot wheel encoders as well as a gyroscope as supplementary sensors. The proposed method is robust in various challenging indoor environments which contain low-textured areas, moving people, or changing environments. In this work, vanishing point (VP) and line features are utilized as landmarks for SLAM. The orientation of a robot is directly estimated using the direction of the VP. Then the estimation models for the robot position and the line landmark are derived as simple linear equations. Using these models, the camera poses and landmark positions are efficiently corrected by a novel local map correction method. To achieve high accuracy in a long-term exploration, a probabilistic loop detection procedure and a pose correction procedure are performed when the robot revisits the previously mapped areas. The performance of the proposed method is demonstrated under various challenging environments using dataset-based experiments using a desktop computer and real-time experiments using a low-cost embedded system. The experimental environments include a real home-like setting and a dedicated Vicon motion-tracking systems equipped space. These conditions contain low-textured areas, moving people, or changing environments. The proposed method is also tested using the RAWSEEDS benchmark dataset.Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Objectives 10 1.3 Contributions 11 1.4 Organization 12 Chapter 2 Previous works 13 Chapter 3 Methodology 17 3.1 System overview 17 3.2 Manhattan grid and system initialization 23 3.3 Vanishing point based robot orientation estimation 25 3.4 Line landmark position estimation 29 3.5 Camera position estimation 35 3.6 Local map correction 37 3.7 Loop closing 40 3.7.1 Extracting multiple BRIEF-Gist descriptors 40 3.7.2 Data structure for fast comparison 43 3.7.3 Bayesian filtering based loop detection 45 3.7.4 Global pose correction 47 Chapter 4 Experiments 49 4.1 Home environment dataset 51 4.2 Vicon dataset 60 4.3 Benchmark dataset in large scale indoor environment 74 4.4 Embedded real-time SLAM in home environment 79 Chapter 5 Conclusion 82 Appendix: performance evaluation of various loop detection methods in home environmnet 84 Reference 90Docto