Visual Location Method Based on Asymmetric Guiding Light Array in UUV Recovery Progress Visionbased recovery of the unmanned underwater vehicle(UUV) is a hard task due to complex underwater optical conditions. Based on the forkcarryingpole recovery system, a visual positioning method based on asymmetric guiding light array is proposed. In this method, the guiding light array is detected and located by camera, so as to complete the recovery. This paper mainly solves three problems Firstly, an asymmetric Lshaped guiding light array is designed based on the characteristics of underwater optical conditions and forkcarryingpole recovery system; Secondly, in condition of different background light intensity, we proposed a set of detection methods for Lshaped light arrays. According to the change of background light, the threshold segmentation interval can be adaptively selected, and the segmentation threshold of light array image can be selected accurately. The pseudo light source can be eliminated by BLOB feature combined with logic regression. Thirdly, a 6degreeoffreedom(6DOF) pose estimation method of UUV is designed, and a location method of tracking after single Target Segmentation (TASTS) is proposed to improve the location method. This novel TASTS can solve the problem of tracking and positioning of partial target occlusion within 50%. Finally, the UUV recovery experiment is carried out. The experimental results show that this method improves the success rate of positioning, eliminates the close visual blind area of visual recovery. This method improves the accuracy, whose error is reduced from 0.5 m to 0.1 m in the final stage, and increases the rapidity rate of UUV autonomous recovery by 20%.

Visual motion estimation for an autonomous underwater reef monitoring robot Performing reliable localisation and navigation within highly unstructured underwater coral reef environments is a difficult task at the best of times. Typical research and commercial underwater vehicles use expensive acoustic positioning and sonar systems which require significant external infrastructure to operate effectively. This paper is focused on the development of a robust visionbased motion estimation technique using lowcost sensors for performing realtime autonomous and untethered environmental monitoring tasks in the Great Barrier Reef without the use of acoustic positioning. The technique is experimentally shown to provide accurate odometry and terrain profile information suitable for input into the vehicle controller to perform a range of environmental monitoring tasks.