As computer vision algorithms are developed on a continuous basis, the visual information from vision sensors has been widely used in the context of simultaneous localization and mapping (SLAM), called visual SLAM, which utilizes relative motion information between images. This research addresses a visual SLAM framework for online localization and mapping in an unstructured seabed environment that can be applied to a low-cost unmanned underwater vehicle equipped with a single monocular camera as a major measurement sensor. Typically, an image motion model with a predefined dimensionality can be corrupted by errors due to the violation of the model assumptions, which may lead to performance degradation of the visual SLAM estimation. To deal with the erroneous image motion model, this study employs a local bundle optimization (LBO) scheme when a closed loop is detected. The results of comparison between visual SLAM estimation with LBO and the other case are presented to validate the effectiveness of the proposed methodology.

This paper shows the development of a snake robot (KAEROT-snake V) which consists of 16 1-DOF actuator modules and head module. The modules are connected serially and the joint axis of each module is rotated by 90° with respect to the previous joint so that the snake robot can move in the 3D space. A tail actuator module includes slip-ring and metal connector. KAEROT-snake IV developed in prior research could move in the 3D space and climb up in a narrow pipe. But its design was not appropriate to the unstructured tough environment and its speed was somewhat slow. A new actuator module is designed to enclose all parts of the module so that any wire is not exposed. The size and weight of the new module was slightly reduced. And the rotation speed and torque of the joint was increased by about twice when compared with pre-module. An embedded controller was developed so small that it can be mounted inside the module. The performance of the developed robot was demonstrated through various locomotion experiments.