6DOF entropy minimization SLAM

In this paper, we propose and validate an entropy minimization algorithm for solving the SLAM problem in the 6DOF case with semi-sparse (stereo) data. The proposed SLAM solution relies on both an efficient and robust strategy for egomotion estimation and an effective global rectification strategy. Our global rectification method is scalable because it relies on dynamically compressing actions, in order to reduce the number of variables to optimize, and thus on integrating/fusing observations. We have implemented a wearable stereo device that runs the SLAM algorithm in real time and we have tested such implementation both in indoor and outdoor scenarios. Our experiments show that action compression is a critical element for yielding acceptable and efficient solutions to the global optimization problem in the 6DOF case