  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.
