PeRL STUDIES AUTONOMOUS NAVIGATION & MAPPING FOR MOBILE ROBOTS IN A PRIORI UNKNOWN ENVIRONMENTS.
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Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping
Summary
Lu Gan, Ray Zhang, Jessy W. Grizzle, Ryan M. Eustice and Maani Ghaffari, Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping. IEEE Robotics and Automation Letters, 5(2):790-797, 2020.Abstract
This article develops a Bayesian continuous 3D semantic occupancy map from noisy point clouds by generalizing the Bayesian kernel inference model for building occupancy maps, a binary problem, to semantic maps, a multi-class problem. The proposed method provides a unified probabilistic model for both occupancy and semantic probabilities and nicely reverts to the original occupancy mapping framework when only one occupied class exists in obtained measurements. The Bayesian spatial kernel inference relaxes the independent grid assumption and brings smoothness and continuity to the map inference, enabling to exploit local correlations present in the environment and increasing the performance. The accompanying software uses multi-threading and vectorization, and runs at about 2 Hz on a laptop CPU. Evaluations using multiple sequences of stereo camera and LiDAR datasets show that the proposed method consistently outperforms current baselines. We also present a qualitative evaluation using data collected with a bipedal robot platform on the University of Michigan - North Campus.
Bibtex entry
@ARTICLE { ganlu-2020a,
AUTHOR = { Lu Gan and Ray Zhang and Jessy W. Grizzle and Ryan M. Eustice and Maani Ghaffari },
TITLE = { {Bayesian} Spatial Kernel Smoothing for Scalable Dense Semantic Mapping },
JOURNAL = { IEEE Robotics and Automation Letters },
YEAR = { 2020 },
VOLUME = { 5 },
NUMBER = { 2 },
PAGES = { 790-797 },
}