Current simultaneous localization and mapping (SLAM) algorithms perform well in static environments but easily fail in dynamic environments. Recent works introduce deep learning-based semantic information to SLAM systems to reduce the influence of dynamic objects. However, it is still challenging to apply a robust localization in dynamic environments for resource-restricted robots. This paper proposes a real-time RGB-D inertial odometry system for resource-restricted robots in dynamic environments named Dynamic-VINS. Three main threads run in parallel: object detection, feature tracking, and state optimization. The proposed Dynamic-VINS combines object detection and depth information for dynamic feature recognition and achieves performance comparable to semantic segmentation. Dynamic-VINS adopts grid-based feature detection and proposes a fast and efficient method to extract high-quality FAST feature points. IMU is applied to predict motion for feature tracking and moving consistency check. The proposed method is evaluated on both public datasets and real-world applications and shows competitive localization accuracy and robustness in dynamic environments. Yet, to the best of our knowledge, it is the best-performance real-time RGB-D inertial odometry for resource-restricted platforms in dynamic environments for now. The proposed system is open source at: https://github.com/HITSZ-NRSL/Dynamic-VINS.git

翻译：当前同时定位与地图构建（SLAM）算法在静态环境中表现良好，但在动态环境中容易失效。近期研究将基于深度学习的语义信息引入SLAM系统以减少动态物体的影响。然而，对于资源受限的机器人而言，在动态环境中实现鲁棒定位仍具挑战性。本文提出一种面向资源受限机器人在动态环境中的实时RGB-D惯性里程计系统——Dynamic-VINS。该系统并行运行三个主要线程：目标检测、特征跟踪与状态优化。所提出的Dynamic-VINS结合目标检测与深度信息进行动态特征识别，性能可与语义分割相媲美。Dynamic-VINS采用网格化特征检测，并提出一种快速高效的方法提取高质量FAST特征点。IMU被用于预测运动以辅助特征跟踪与运动一致性校验。该方法在公开数据集和实际应用中均进行了评估，在动态环境中展现了有竞争力的定位精度与鲁棒性。据我们所知，这是当前动态环境中面向资源受限平台性能最优的实时RGB-D惯性里程计系统。该系统已开源：https://github.com/HITSZ-NRSL/Dynamic-VINS.git