Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks

• URL: http://arxiv.org/abs/2109.12869v1
• Date: Mon, 27 Sep 2021 08:40:19 GMT
• Title: Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks
• Authors: Jianxiang Feng, Maximilian Durner, Zoltan-Csaba Marton, Ferenc Balint-Benczedi, and Rudolph Triebel
• Abstract summary: This work focuses on improving uncertainty estimation in the field of object classification from RGB images. We employ a (BNN) and evaluate two practical inference techniques to obtain better uncertainty estimates. We show a performance increase using more reliable uncertainty estimates as unary potentials within a Conditional Random Field.
• Score: 17.162534445528827
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work focuses on improving uncertainty estimation in the field of object classification from RGB images and demonstrates its benefits in two robotic applications. We employ a (BNN), and evaluate two practical inference techniques to obtain better uncertainty estimates, namely Concrete Dropout (CDP) and Kronecker-factored Laplace Approximation (LAP). We show a performance increase using more reliable uncertainty estimates as unary potentials within a Conditional Random Field (CRF), which is able to incorporate contextual information as well. Furthermore, the obtained uncertainties are exploited to achieve domain adaptation in a semi-supervised manner, which requires less manual efforts in annotating data. We evaluate our approach on two public benchmark datasets that are relevant for robot perception tasks.

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