LiDAR-inertial odometry (LIO) systems differ in whether they continue estimating gravity after initialization. We compare four gravity-bias state configurations in each of FAST-LIO2 and LIO-SAM, then separately test a gravity-direction factor. Across 12 dataset sequences evaluated with FAST-LIO2, fixing gravity under c...
Jie Xu, Zi-Yi Jin, Kang-Jin Yu et al.· 0 citations
UpDown-SC is presented, a training-free polar descriptor that first canonicalizes gravity and then represents two complementary surfaces: the upper envelope of lower/middle structures and the lower envelope of overhead structures that gives the best or second-best F1max and AUPR under threshold-based acceptance while r...
Jie Xu, Yong-Xin Yang, Zi-Yi Jin et al.· 0 citations
GIFT (Guided Intermediate Feature Training), an architecture-flexible framework for learning intermediate features that translates these structures into training-time constraints through geometry alignment, affordance prediction, and goal-region reconstruction, establishes learning functionally structured intermediate...
Yu-Peng Zheng, Xiang Li, Song-En Gu et al.· 2 citations
JEPA Policy is introduced, a diffusion-free framework that uses the action chunk and its observed future representation as paired training targets as paired training targets and supports paired future-representation supervision supervision as a practical approach to low-latency visuomotor imitation without iterative ge...
Jie Xu, Kang-Jin Yu, Zi-Yi Jin et al.· 0 citations
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