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Online Learning of a Probabilistic and Adaptive Scene Representation
[article]
2021
arXiv
pre-print
Constructing and maintaining a consistent scene model on-the-fly is the core task for online spatial perception, interpretation, and action. In this paper, we represent the scene with a Bayesian nonparametric mixture model, seamlessly describing per-point occupancy status with a continuous probability density function. Instead of following the conventional data fusion paradigm, we address the problem of online learning the process how sequential point cloud data are generated from the scene
arXiv:2103.16832v1
fatcat:qx3ncxz7rnhqbidvxqqvoqswoq