In this paper we investigate the application of semi-dense visual Simultaneous Localisation and Mapping (SLAM) to the humanoid robotics domain. Challenges of visual SLAM applied to humanoids include the type of dynamic motion executed by the robot, a lack of features in man-made environments and the presence of dynamics in the scene. Previous research on humanoid SLAM focused mostly on feature-based methods which result in sparse environment reconstructions. Instead, we investigate the application of a modern direct method to obtain a semi-dense visually interpretable map which can be used for collision free motion planning. We tackle the challenge of using direct visual SLAM on a humanoid by proposing a more robust pose tracking method. This is formulated as an optimisation problem over a cost function which combines information from the stereo camera and a low-drift kinematic-inertial motion prior. Extensive experimental demonstrations characterise the performance of our method using the NASA Valkyrie humanoid robot in a laboratory environment equipped with a Vicon motion capture system. Our experiments demonstrate pose tracking robustness to challenges such as sudden view change, motion blur in the image, change in illumination and tracking through sequences of featureless areas in the environment. Finally, we provide a qualitative evaluation of our stereo reconstruction against a LIDAR map.