  This paper reports on a robust RGB-D SLAM system that performs well in
scarcely textured and structured environments. We present a novel
keyframe-based continuous visual odometry that builds on the recently developed
continuous sensor registration framework. A joint geometric and appearance
representation is the result of transforming the RGB-D images into functions
that live in a Reproducing Kernel Hilbert Space (RKHS). We solve both
registration and keyframe selection problems via the inner product structure
available in the RKHS. We also extend the proposed keyframe-based odometry
method to a SLAM system using indirect ORB loop-closure constraints. The
experimental evaluations using publicly available RGB-D benchmarks show that
the developed keyframe selection technique using continuous visual odometry
outperforms its robust dense (and direct) visual odometry equivalent. In
addition, the developed SLAM system has better generalization across different
training and validation sequences; it is robust to the lack of texture and
structure in the scene; and shows comparable performance with the
state-of-the-art SLAM systems.
