稳健性(进化)
人工智能
计算机科学
计算机视觉
视觉里程计
里程计
高动态范围
公制(单位)
管道(软件)
动态范围
机器人
移动机器人
工程类
基因
生物化学
运营管理
化学
程序设计语言
作者
Zichao Zhang,Christian Förster,Davide Scaramuzza
标识
DOI:10.1109/icra.2017.7989449
摘要
We propose an active exposure control method to improve the robustness of visual odometry in HDR (high dynamic range) environments. Our method evaluates the proper exposure time by maximizing a robust gradient-based image quality metric. The optimization is achieved by exploiting the photometric response function of the camera. Our exposure control method is evaluated in different real world environments and outperforms the built-in auto-exposure function of the camera. To validate the benefit of our approach, we adapt a state-of-the-art visual odometry pipeline (SVO) to work with varying exposure time and demonstrate improved performance using our exposure control method in challenging HDR environments.
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