Deep Visual Odometry Methods for Mobile Robots
원문 초록
Technology has made navigation in 3D real time possible and this has made possible what seemed impossible. This paper explores the aspect of deep visual odometry methods for mobile robots. Visual odometry has been instrumental in making this navigation successful. Noticeable challenges in mobile robots including the inability to attain Simultaneous Localization and Mapping have been solved by visual odometry through its cameras which are suitable for human environments. More intuitive, precise and accurate detection have been made possible by visual odometry in mobile robots. Another challenge in the mobile robot world is the 3D map reconstruction for exploration. A dense map in mobile robots can facilitate for localization and more accurate findings.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
InCrowd-VI: A Realistic Visual-Inertial Dataset for Evaluating SLAM in Indoor Pedestrian-Rich Spaces for Human Navigation
LGU-SLAM: Learnable Gaussian Uncertainty Matching with Deformable Correlation Sampling for Deep Visual SLAM
ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras
cuVSLAM: CUDA accelerated visual odometry and mapping
SafeNav: Safe Path Navigation using Landmark Based Localization in a GPS-denied Environment
MonoGS++: Fast and Accurate Monocular RGB Gaussian SLAM
토론
이 논문과 연결된 포럼 글이 아직 없습니다.