A learning-free cross-FOV ensemble plus certainty-coupled GICP keeps place recognition and 6-DoF localization usable under extreme FOV asymmetry and wide-to-narrow cross-sensor pairing where prior methods collapse.
University of Michigan North Campus long-term vision and lidar dataset
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.RO 2years
2026 2representative citing papers
PROBE models BEV occupancy as Bernoulli variables, analytically marginalizes Cartesian translations via a polar Jacobian, and reports top handcrafted multi-session place-recognition accuracy across four LiDAR types.
citing papers explorer
-
G-PROBE: Cross-FOV Place Recognition and Certainty-Coupled Localization for 3D Point Clouds
A learning-free cross-FOV ensemble plus certainty-coupled GICP keeps place recognition and 6-DoF localization usable under extreme FOV asymmetry and wide-to-narrow cross-sensor pairing where prior methods collapse.
-
PROBE: Probabilistic Occupancy BEV Encoding with Analytical Translation Robustness for 3D Place Recognition
PROBE models BEV occupancy as Bernoulli variables, analytically marginalizes Cartesian translations via a polar Jacobian, and reports top handcrafted multi-session place-recognition accuracy across four LiDAR types.