Dense Piecewise Planar Reconstruction based on Low Gradient Region Depth Estimation from a Monocular Image Sequence

• Main Authors: Jian-hao Huang, 黃建豪
• Other Authors: 連豊力
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/ujhn5v

碩士 === 國立臺灣大學 === 電機工程學研究所 === 106 === Visual navigation of robot has been a popular and a challenge research topic in past few years. One of important part for navigation is environment sensing. Especially for previously unknown and GPS-denied environments, this thesis uses monocular camera to obtain image data and estimates the depth map information in each keyframe by LSD SLAM [11: Engel et al. 2014]. RGB image and depth map in each keyframe are extracted to detect low texture regions by region growing segmentation method. The assumption made is that image areas with low photometric gradients are mostly planar which is met in most indoors and man-made scene. This thesis proposes a depth filling method to optimize the depth map completeness in each keyframe. It can provide robot more environment information to apply on navigation. For monocular unknown scalar problem, the assigned marker in the scene is used to compute the scale. However, the estimated scale is used to define the thresholds that are used to filter out the unreasonable plane estimation in depth filling process. This thesis compares the depth filling method against several alternatives using Gazebo simulation [35: Gazebo from OSRF, Inc], public Tum dataset [23: Sturm et al. 2012], and experiment with a Microsoft Kinect sensor. The comparison demonstrate that our depth filling method for piecewise planar monocular SLAM is denser than LSD SLAM [11: Engel et al. 2014] and DPPTAM [12: Concha & Civera 2015].