Monocular SLAM Supported Object Recognition


In this work, we develop a monocular SLAM-aware object recognition system that is able to achieve considerably stronger recognition performance, as compared to classical object recognition systems that function on a frame-by-frame basis. By incorporating several key ideas including multi-view object proposals and efficient feature encoding methods, our proposed system is able to detect and robustly recognize objects in its environment using a single RGB camera in near-constant time. Through experiments, we illustrate the utility of using such a system to effectively detect and recognize objects, incorporating multiple object viewpoint detections into a unified prediction hypothesis. The performance of the proposed recognition system is evaluated on the UW RGB-D Dataset, showing strong recognition performance and scalable run-time performance compared to current state-of-the-art recognition systems.

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Fig. 1: The proposed SLAM-aware object recognition system is able to robustly localize and recognize several objects in the scene, by aggregating object detection evidence across multiple views.

Publications

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Monocular SLAM Supported Object Recognition
S. Pillai and J. Leonard
Robotics: Science and Systems (RSS), 2015
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Code

The FLAIR implementation used in the paper is available on github.