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arXiv · 1910.07093

Explainable Semantic Mapping for First Responders

Jean Oh·Martial Hebert·Hae-Gon Jeon·Xavier Perez·Chia Dai·Yeeho Song
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Abstract

One of the key challenges in the semantic mapping problem in postdisaster environments is how to analyze a large amount of data efficiently with minimal supervision. To address this challenge, we propose a deep learning-based semantic mapping tool consisting of three main ideas. First, we develop a frugal semantic segmentation algorithm that uses only a small amount of labeled data. Next, we investigate on the problem of learning to detect a new class of object using just a few training examples. Finally, we develop an explainable cost map learning algorithm that can be quickly trained to generate traversability cost maps using only raw sensor data such as aerial-view imagery. This paper presents an overview of the proposed idea and the lessons learned.

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Jean Oh, Martial Hebert, Hae-Gon Jeon, Xavier Perez, Chia Dai, Yeeho Song. 2019-10-15. Explainable Semantic Mapping for First Responders. https://arxiv.org/abs/1910.07093

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