Visual localization has been well studied in recent decades and applied in many fields as a fundamental capability in robotics. However, the success of the state of the arts usually builds on the assumption that the environment is static. In dynamic scenarios where moving objects are present, the performance of the existing visual localization systems degrades a lot due to the disturbance of the dynamic factors. To address this problem, we propose a novel sparse motion removal (SMR) model that detects the dynamic and static regions for an input frame based on a Bayesian framework. The similarity between the consecutive frames and the difference between the current frame and the reference frame are both considered to reduce the detection uncertainty. After the detection process is finished, the dynamic regions are eliminated while the static ones are fed into a feature-based visual simultaneous localization and mapping (SLAM) system for further visual localization. To verify the proposed method, both qualitative and quantitative experiments are performed and the experimental results have demonstrated that the proposed model can significantly improve the accuracy and robustness for visual localization in dynamic environments.
Note to Practitioners—This article was motivated by the visual localization problem in dynamic environments. Visual localization is well applied in many robotic fields such as path planning and exploration as the basic capability for a mobile robot. In the GPS-denied environments, one robot needs to localize itself through perceiving the unknown environment based on a visual sensor. In real-world scenes, the existence of the moving objects will significantly degrade the localization accuracy, which makes the robot implementation unreliable. In this article, an SMR model is designed to handle this problem. Once receiving a frame, the proposed model divides it into dynamic and static regions through a Bayesian framework. The dynamic regions are eliminated, while the static ones are maintained and fed into a feature-based visual SLAM system for further visual localization. The proposed method greatly improves the localization accuracy in dynamic environments and guarantees the robustness for robotic implementation.
中文翻译：
基于稀疏运动去除的动态环境中的稳健视觉定位
近几十年来，视觉本地化已经得到了很好的研究，并已作为机器人技术的基本功能应用于许多领域。但是，现有技术的成功通常基于环境是静态的假设。在存在移动物体的动态场景中，由于动态因素的干扰，现有视觉定位系统的性能会大大降低。为了解决这个问题，我们提出了一种新颖的稀疏运动去除（SMR）模型，该模型基于贝叶斯框架检测输入帧的动态和静态区域。连续帧之间的相似性以及当前帧和参考帧之间的差异都被认为可以减少检测的不确定性。检测过程完成后，动态区域被消除，而静态区域被馈送到基于特征的视觉同时定位和制图（SLAM）系统中，以进行进一步的视觉定位。为了验证所提出的方法，进行了定性和定量实验，实验结果表明，所提出的模型可以显着提高动态环境中视觉定位的准确性和鲁棒性。
执业者注意—本文的灵感来自动态环境中的视觉本地化问题。视觉本地化已很好地应用于许多机器人领域，例如路径规划和探索作为移动机器人的基本功能。在GPS受限的环境中，一个机器人需要通过基于视觉传感器感知未知环境来对自身进行定位。在现实世界的场景中，运动对象的存在会大大降低定位精度，从而使机器人的实现不可靠。在本文中，SMR模型旨在解决此问题。一旦收到帧，建议的模型就会通过贝叶斯框架将其分为动态和静态区域。动态区域被消除，静态的则被维护并馈入基于功能的视觉SLAM系统中，以进行进一步的视觉定位。所提出的方法大大提高了动态环境中的定位精度，并保证了机器人实现的鲁棒性。
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