We are proposing an adaptation of the gradient descent method to optimize the position and orientation of sensors for the sensor placement problem. The novelty of the proposed method lies in the combination of gradient descent optimization with a realistic model, which considers both the topography of the environment and a set of sensors with directional probabilistic sensing. The performance of this approach is compared with two other black box optimization methods over area coverage and processing time. Results show that our proposed method produces competitive results on smaller maps and superior results on larger maps, while requiring much less computation than the other optimization methods to which it has been compared.

The accuracy of 3D reconstruction for metrology applications using active stereo pairs depends on the quality of the calibration of the system. Active stereo pairs are generally composed of cameras mounted on tilt/pan mechanisms separated by a constant or variable baseline. This paper presents a calibration approach based on speckle metrology that allows the separation of translation and rotation in the estimation of extrinsic parameters. To achieve speckle-based calibration, a device called an Almost Punctual Speckle Source (APSS) is introduced. Using the APSS, a thorough method for the calibration of extrinsic parameters of stereo pairs is described. Experimental results obtained with a stereo system called the Agile Stereo Pair (ASP) demonstrate that speckle-based calibration achieves better reconstruction performance than methods using standard calibration procedures. Although the experiments were performed with a specific stereo pair, such as the ASP, which is described in the paper, the speckle-based calibration approach using the APSS can be transposed to other stereo setups.