Improving RGB-D Visual Odometry with Depth Learned from a Better Sensor’s Output

Abstract

This paper compares the results obtained from an indoor Visual Odometry (VO) system with RGB-D images provided by a Kinect v1 camera against those achieved by a VO with enhanced depth channel. For this purpose, we have used two classic image inpainting methods and a deeplearning approach for scene depth estimation employing Kinect v2 depth maps as reference data. The ability to enhance lower-quality data is crucial to reduce the cost of VO applications because higher-quality information can be infused through deep learning in systems using budget sensors.