A rasterizer-integrated Fisher approximation that quantifies per-primitive sensitivity online is introduced, serving as an information-driven heuristic for Gaussian pruning and next-best-view planning and achieves real-time, high-fidelity and uncertainty-aware mapping at scale.
Bo-Xin Bu, Simon Boche, Hao-Ming Zhang et al.· 0 citations
Legged robots offer superior mobility in unstructured environments, but reliable operation in such conditions requires robust state estimation. To address the vulnerability of proprioceptive estimators in rough terrain, recent methods have incorporated radar to provide velocity measurements. However, their limited yaw...
Hanjun Kim, Chiyun Noh, Sang-Woo Jung et al.· IEEE Robotics and Automation...· 0 citations
Robots operating in dark or poorly lit environments rely on onboard lights, which often produce uneven illumination that degrades downstream perception tasks. Prior approaches based on 2D image enhancement lack reliable supervision and fail to preserve multi-view geometric consistency. To address these limitations, we...
Liang Hong, Jia-Xin Wei, Simon Schaefer et al.· 0 citations
Scalix is proposed, a real-time monocular SLAM framework that achieves metric-scale state estimation by integrating learned depth cues into a probabilistic factor-graph formulation, leading to improved scale consistency through multi-view data associations.
