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.

本文研究了一种单目 SLAM 感知的目标识别系统，该系统利用多视图对象提议和高效特征编码等方法，能够在几乎恒定的时间内使用单个 RGB 相机检测和识别环境中的对象。实验结果表明，该系统具有强大的识别功能，并在 UW RGB-D 数据集上表现出可扩展的运行时性能优势。