SNU Robot Vision Lab

연구실 진학을 희망하는 학생은 원서 접수 기간 한 달 전까지 아래 링크를 통해 면담신청서를 작성해 주시기 바랍니다. In this paper we propose a robust visual odometry system for a wide-baseline camera rig with wide field-of-view (FOV) fisheye lenses, which provides full omnidirectional stereo observations of the environment. For more robust and accurate ego-motion estimation we adds three components to the standard VO pipelines, 1) the hybrid projection model for improved feature matching, 2) multi-view p3p ransanc algorithm for pose estimation, and 3) online update of rig extrinsic parameters. The proposed system is extensively evaluated with synthetic datasets with ground-truth and real sequences of highly dynamic environment, and its superior performance is demonstrated. Visual inertial odometry (VIO) gained lots of interest recently for efficient and accurate ego-motion estimation of robots and automobiles. With a monocular camera and an inertial measurement unit (IMU) rigidly attached, VIO aims to estimate the 3D pose trajectory of the device in a global metric space. We propose a novel visual inertial odometry algorithm which directly optimizes the camera poses with noisy IMU data and visual feature locations. Building the map of environment for localization and navigation is critical for scene understanding and robot operation. We propose a metric-topological mapping which holds the benefits of both metric maps and topological maps. Points are commonly used for structure from motion and ego-motion estimation. We investigated more robust and fast ways to use line features for motion estimation of a stereo camera rig. In this paper, we propose a robust dense stereo reconstruction algorithm using a random walk with restart. The pixel-wise matching costs are aggregated into superpixels and the modified random walk wit