Accurate Localization
Accurate localization, the precise determination of an object's position and orientation, is a crucial challenge across diverse fields, driving research into robust and efficient methods. Current efforts focus on developing advanced sensor fusion techniques (e.g., integrating LiDAR, radar, IMU, and cameras), employing sophisticated algorithms like graph optimization and deep learning models (e.g., transformers, neural radiance fields), and addressing challenges like uncertainty quantification and dynamic environments. These advancements have significant implications for autonomous systems (robotics, vehicles), medical imaging, and other applications requiring precise spatial awareness.
Papers
An Adaptive Indoor Localization Approach Using WiFi RSSI Fingerprinting with SLAM-Enabled Robotic Platform and Deep Neural Networks
Seyed Alireza Rahimi Azghadi, Atah Nuh Mih, Asfia Kawnine, Monica Wachowicz, Francis Palma, Hung Cao
Accurate Prior-centric Monocular Positioning with Offline LiDAR Fusion
Jinhao He, Huaiyang Huang, Shuyang Zhang, Jianhao Jiao, Chengju Liu, Ming Liu
MapLocNet: Coarse-to-Fine Feature Registration for Visual Re-Localization in Navigation Maps
Hang Wu, Zhenghao Zhang, Siyuan Lin, Xiangru Mu, Qiang Zhao, Ming Yang, Tong Qin
Improving Visual Place Recognition Based Robot Navigation Through Verification of Localization Estimates
Owen Claxton, Connor Malone, Helen Carson, Jason Ford, Gabe Bolton, Iman Shames, Michael Milford