论文标题
Dynavins：动态环境的视觉惯性大满贯
DynaVINS: A Visual-Inertial SLAM for Dynamic Environments
论文作者
论文摘要
视觉惯性探测器和猛击算法被广泛用于各个领域，例如服务机器人，无人机和自动驾驶汽车。大多数SLAM算法是基于地标是静态的。但是，在现实世界中，存在各种动态对象，它们会降低姿势估计准确性。另外，暂时的静态对象，在观察过程中是静态的，但在视线视线时移动，会触发假循环封闭。为了克服这些问题，我们提出了一个新颖的视觉惯性大满贯框架，称为dynavins，它对动态对象和暂时静态对象都具有强大的态度。在我们的框架中，我们首先提出一个可靠的捆绑捆绑调整，该调整可以通过利用由IMU预先整合估计的姿势先验来拒绝动态对象的特征。然后，提出了一个密钥帧分组和基于多刺的约束分组方法，以减少循环闭合中暂时静态对象的效果。随后，我们在包含许多动态对象的公共数据集中评估了我们的方法。最后，通过成功拒绝动态和暂时静态对象的效果，与其他最先进的方法相比，我们的测力量与其他最先进方法相比，我们的测力素具有有希望的性能得到证实。我们的代码可在https://github.com/url-kaist/dynavins上找到。
Visual inertial odometry and SLAM algorithms are widely used in various fields, such as service robots, drones, and autonomous vehicles. Most of the SLAM algorithms are based on assumption that landmarks are static. However, in the real-world, various dynamic objects exist, and they degrade the pose estimation accuracy. In addition, temporarily static objects, which are static during observation but move when they are out of sight, trigger false positive loop closings. To overcome these problems, we propose a novel visual-inertial SLAM framework, called DynaVINS, which is robust against both dynamic objects and temporarily static objects. In our framework, we first present a robust bundle adjustment that could reject the features from dynamic objects by leveraging pose priors estimated by the IMU preintegration. Then, a keyframe grouping and a multi-hypothesis-based constraints grouping methods are proposed to reduce the effect of temporarily static objects in the loop closing. Subsequently, we evaluated our method in a public dataset that contains numerous dynamic objects. Finally, the experimental results corroborate that our DynaVINS has promising performance compared with other state-of-the-art methods by successfully rejecting the effect of dynamic and temporarily static objects. Our code is available at https://github.com/url-kaist/dynaVINS.
