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Semantic Map Building Based on Object Detection for Indoor Navigation
Yang, Jinfu Zhang, Jizhao Wang, Guanghui Li, Mingai
Yang, Jinfu
Zhang, Jizhao
Wang, Guanghui
Li, Mingai
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Abstract
Building a map of the environment is a prerequisite for mobile robot navigation. In this paper, we present a semantic map building method for indoor navigation of a robot using only the image sequence acquired by a mon‐ ocular camera installed on the robot. First, a topological map of the environment is created, where each key frame forms a node of the map represented as visual words (VWs). The edges between two adjacent nodes are built from relative poses obtained by performing a novel pose estimation approach, called one-point RANSAC camera pose estimation (ORPE). Then, taking advantage of an improved deformable part model (iDPM) for object detection, the topological map is extended by assigning semantic attributes to the nodes. Extensive experimental evaluations demonstrate the effectiveness of the proposed monocular SLAM method.
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Date
2015-11-23
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Publisher
SAGE Publications
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Keywords
Semantic map, Topological map, Object detection, Deformable part model, Monocular SLAM
Citation
Yang, Jinfu, Jizhao Zhang, Guanghui Wang, and Mingai Li. "Semantic Map Building Based on Object Detection for Indoor Navigation." International Journal of Advanced Robotic Systems (2015): 1.