## 1 Introduction  
Exploitation and preservation of groundwater resources are very important with many challenges related to economic development, social constraints and mainly environment protection. In this context, hydro-geologists are, for example, highly attracted by the interest of confined underwater environments. They mainly address the question of dynamic modeling of such complex geological structure (the karst). In this domain, robotic is of great interest, and many different hot topics in terms of control, navigation, sensing and actuation are addressed. On the sensing aspect, robotic exploration of confined environment requires sensors providing pertinent input information to supply the control loop. In this scope, mechanical profiling sonars exist but they provide a too low sampling rate to be used directly in the robot control loop. That is why the use of vision techniques is the most suitable solution even though it presents a more demanding constraint in terms of quantity of information to be processed.  
Different visual perception devices are proposed in literature, offering a large view of the environment. However, a trade-off between images resolution, cost, efficiency and real time processing has to be taken into consideration. The use of cameras and computer vision techniques represents a common choice for autonomous scene reconstruction. Nevertheless, the computational complexity and large amount of data make real-time processing of stereo vision challenging because of the inherent instruction cycle delay within conventional computers [1]. In this case, custom architecture implementation on FPGA (Field Programmable Gate Arrays) is one of the most recommended solutions.  
It permits to exploit the intrinsic parallelism of the 3D reconstruction algorithms and to optimize memory access patterns in order to ensure real-time 3D image reconstruction [2]. By taking advantage of the resources integrated into modern FPGA circuits, dedicated architectures are designed with more hardware efficiency and time performances. In addition, due to its reconfigurability, FPGA allow the architecture to be modified and adapted to the application requirements. It can be optimized depending on the implemented task, unlike conventional processors or Digital Signal Processors.  
In the field of computer vision applications, 3D reconstruction algorithms are nowadays of great interest and could be exploited in different embedded systems and real-time applications such as geographic information systems, satellite-based earth and space exploration, autonomous robots and security systems. In this paper, we propose a 3D shape reconstruction algorithm for underwater confined environment (karst) exploration using stereoscopic images and its pipelined hardware architecture design. The hardware implementation is performed on a Virtex-6 FPGA using the Xilinx System Generator (XSG) development tool. We aim to propose a high efficiency system in terms of level of accuracy and execution time performances.  
The rest of the paper is organized as follows. In Section 2 we present and discuss various existing image processing methods that focus on laser spot detection, matching and 3D reconstruction. In Section 3, we present the vision-based sensing device and its calibration. A flowchart of the proposed algorithm and the key processes details are presented in Section 4. Section 5 presents the hardware XSG design of the 3D reconstruction process. In Section 6, we present the experimental and performance evaluation results. Finally, conclusions and future works are presented in the last section.