4 accepted papers
LiDAR-based global localization provides accurate robot pose estimates against a prior map. Existing deep-learning methods, however, demand heavy computation and long training or inference times and degrade sharply when faced with domain shifts. This letter presents LighterBEV, a lightweight, fast,
LiDAR-based global localization provides accurate robot pose estimates against a prior map. Existing deep-learning methods, however, demand heavy computation and long training or inference times and degrade sharply when faced with domain shifts. This letter presents LighterBEV, a lightweight, fast, …
LiDAR place recognition is a critical component of LiDAR-based localization pipelines, tasked with identifying previously visited places across diverse environments and temporal conditions. A growing body of deep learning–based approaches has recently tackled this problem. However, their performance…
LiDAR Place Recognition (LPR) aims to identify previously visited places across different environments and times. Thanks to the recent advances in Deep Neural Networks (DNNs), LPR has experienced rapid development. However, DNN-based LPR methods may suffer from Catastrophic Forgetting (CF), where th