  Detection of moving objects is an essential capability in dealing with
dynamic environments. Most moving object detection algorithms have been
designed for color images without depth. For robotic navigation where real-time
RGB-D data is often readily available, utilization of the depth information
would be beneficial for obstacle recognition.
  Here, we propose a simple moving object detection algorithm that uses RGB-D
images. The proposed algorithm does not require estimating a background model.
Instead, it uses an occlusion model which enables us to estimate the camera
pose on a background confused with moving objects that dominate the scene. The
proposed algorithm allows to separate the moving object detection and visual
odometry (VO) so that an arbitrary robust VO method can be employed in a
dynamic situation with a combination of moving object detection, whereas other
VO algorithms for a dynamic environment are inseparable. In this paper, we use
dense visual odometry (DVO) as a VO method with a bi-square regression weight.
Experimental results show the segmentation accuracy and the performance
improvement of DVO in the situations. We validate our algorithm in public
datasets and our dataset which also publicly accessible.
