Motion blur is one of the major challenges remaining for visual odometry methods. In low-light conditions where longer exposure times are necessary, motion blur can appear even for relatively slow camera motions. In this paper we present a novel hybrid visual odometry pipeline with direct approach that explicitly models and estimates the camera's local trajectory within the exposure time. This allows us to actively compensate for any motion blur that occurs due to the camera motion. In addition, we also contribute a novel benchmarking dataset for motion blur aware visual odometry. In experiments we show that by directly modeling the image formation process, we are able to improve robustness of the visual odometry, while keeping comparable accuracy as that for images without motion blur.
MBA-SLAM: Motion Blur Aware Gaussian Splatting SLAM
College of Computer Science and Technology at Zhejiang University（浙江大学计算机科学与技术学院）；School of Engineering at Westlake University（西湖大学工程学院）；School of Engineering, Westlake University（西湖大学工程学院）
Salient Sparse Visual Odometry With Pose-Only Supervision
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；State University of New York at Buffalo（纽约州立大学布法罗分校）
Li Siguang School, China University of Geosciences, Wuhan（李四光学院，中国地质大学（武汉））；Institute of Artificial Intelligence, Huazhong University of Science and Technology, Wuhan（人工智能研究院，华中科技大学）；School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan（电子信息与通讯学院，华中科技大学）；School of Optoelectronic Engineering, Xidian University, Xian（光电工程学院，西安电子科技大学）
AERO-VIS: Asynchronous Event-based Real-time Onboard Visual-Inertial SLAM
Mobile Robotics Lab, ETH Zürich（苏黎世联邦理工学院移动机器人实验室）；Technical University of Munich（慕尼黑技术大学）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）
Event-based SLAM Benchmark for High-Speed Maneuvers
Neuromorphic Automation and Intelligence Lab (NAIL) at the School of Artificial Intelligence and Robotics, Hunan University, Changsha, China（神经形态自动化与智能实验室（NAIL）位于湖南大学人工智能与机器人学院，长沙，中国）；Department of Electrical Engineering and Computer Science, Technische Universität Berlin, the Science of Intelligence Excellence Cluster, the Robotics Institute Germany and the Einstein Center Digital Future, Berlin, Germany（柏林技术大学电气工程与计算机科学系，智能科学卓越中心，德国机器人研究所，数字未来爱因斯坦中心，柏林，德国）；College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China（国防科技大学智能科学与技术学院，长沙，中国）；School of Artificial Intelligence and Robotics, Hunan University, Changsha China（湖南大学人工智能与机器人学院，长沙中国）；Robotics and Perception Group, Department of Informatics, University of Zurich, Switzerland（苏黎世大学信息学院机器人与感知组，瑞士）
Online Adaptation of Visual Odometry Frontends with Image-Conditioned Reinforcement Learning
Robotics and Perception Group, University of Zurich, Switzerland（苏黎世大学机器人与感知组，瑞士）；Jet Propulsion Laboratory, California Institute of Technology, USA（加州理工学院喷气推进实验室，美国）
MomentBA: Second-order Spatial Moments for Anisotropic Correspondence Uncertainty in Differentiable Bundle Adjustment
Wuhan University（武汉大学）；Hubei Technology Innovation Center for Spatiotemporal Information and Positioning Navigation（湖北时空信息与定位导航技术创新中心）；Hubei Luojia Laboratory（湖北珞珈实验室）
