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.


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.



Monocular SLAM Supported Object Recognition
S. Pillai and J. Leonard
Robotics: Science and Systems (RSS), 2015
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The FLAIR implementation used in the paper is available on github.