Robust dense visual SLAM using sensor fusion and motion segmentation
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Visual simultaneous localisation and mapping (SLAM) is an important technique for enabling mobile robots to navigate autonomously within their environments. Using cameras, robots reconstruct a representation of their environment and simultaneously localise themselves within it. A dense visual SLAM system produces a high-resolution and detailed reconstruction of the environment which can be used for obstacle avoidance or semantic reasoning. State-of-the-art dense visual SLAM systems demonstrate robust performance and impressive accuracy in ideal conditions. However, these techniques are based on requirements which limit the extent to which they can be deployed in real applications. Fundamentally, they require constant scene illumination, smooth camera motion and no moving objects being present in the scene. Overcoming these requirements is not trivial and significant effort is needed to make dense visual SLAM approaches more robust to real-world conditions. The objective of this thesis is to develop dense visual SLAM systems which are more robust to real-world visually challenging conditions. For this, we leverage sensor fusion and motion segmentation for situations where camera data is unsuitable. The first contribution is a visual SLAM system for the NASA Valkyrie humanoid robot which is robust to the robot’s operation. It is based on a sensor fusion approach which combines visual SLAM and leg odometry to demonstrate increased robustness to illumination changes and fast camera motion. Second, we research methods for robust visual odometry in the presence of moving objects. We propose a formulation for joint visual odometry and motion segmentation that demonstrates increased robustness in scenes with moving objects compared to state-of-the-art approaches. We then extend this method using inertial information from a gyroscope to compare the contributions of motion segmentation and motion prior integration for robustness to scene dynamics. As part of this study we provide a dataset recorded in scenes with different numbers of moving objects. In conclusion, we find that both motion segmentation and motion prior integration are necessary for achieving significantly better results in real-world conditions. While motion priors increase robustness, motion segmentation increases the accuracy of the reconstruction results through filtering of moving objects.