  While dense visual SLAM methods are capable of estimating dense
reconstructions of the environment, they suffer from a lack of robustness in
their tracking step, especially when the optimisation is poorly initialised.
Sparse visual SLAM systems have attained high levels of accuracy and robustness
through the inclusion of inertial measurements in a tightly-coupled fusion.
Inspired by this performance, we propose the first tightly-coupled dense
RGB-D-inertial SLAM system.
  Our system has real-time capability while running on a GPU. It jointly
optimises for the camera pose, velocity, IMU biases and gravity direction while
building up a globally consistent, fully dense surfel-based 3D reconstruction
of the environment. Through a series of experiments on both synthetic and real
world datasets, we show that our dense visual-inertial SLAM system is more
robust to fast motions and periods of low texture and low geometric variation
than a related RGB-D-only SLAM system.
