3 accepted papers
In an era dominated by multi-sensor fusion, this paper explores the operational limits of LiDAR-only odometry. We introduce Onion-LO++, which is designed to overcome two practical limitations of Onion-LO: poor performance in geometrically degenerate environments and instability under high-motion con…
LiDAR odometry is a fundamental technology for autonomous navigation. However, existing LiDAR-based odometry methods typically demand extensive manual parameter tuning and remain prone to instability when deployed across varying LiDAR types and environments. This letter focuses on the essence of poi…
Current multimodal learning strategies primarily optimize in the original token space. Such a framework is easy to incorporate with the backbone of pretrained language model, but might result in modality collapse. To alleviate such issues, we leverage the Joint-Embedding Predictive Architecture (JEP…