  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
