Abstract. In this article, we are interested in the implementation of an open-source low-level architecture (critical system) adapted to agile and easily replicable close-range remote sensing robots operating in multiple evolution domains. After reviewing the existing autopilots responding to these needs, we discuss the available hardware solutions and their limits. Then, we propose an original solution (software and hardware) that we developed to obtain a universal low-level architecture for all our exploration robots, whatever their environment of evolution, and the steps needed to make it run on our chosen family of micro-controllers: the ESP32. Finally, we present the operational results obtained on our different platforms (land, surface, submarine and air), their limits and the envisaged perspectives.

Abstract. In this article, we study the interest of PiCam and its possibilities offered for the realization of a light payload (small and inexpensive) in order to perform the 3D reconstruction of dynamic scenes (underwater or aerial) in close-range remote sensing. We see that on these observation scales, movements of the scenes due to flora and fauna cannot be ignored if we want these objects to be part of the final model. We review the sensors used in the literature for 3D reconstruction and then present the arguments in favor of PiCam with regard to the constraints posed by the use of light and agile vectors. The main issue is the synchronization of these low cost sensors, which is not native: we explain the different steps to obtain a satisfactory synchronization rate with regard to the dynamism of the studied scenes and present the results obtained.

The 3D reconstruction of underwater scenes from overlapping images requires modeling the sensor. While underwater self-calibration gives good results when coupled with multi-view algorithms, calibration or pre-calibration with a pattern is still necessary when scenes are weakly textured or if there are not enough points of view of the same points; however, detecting patterns on underwater images or obtaining a good distribution of these patterns on a dataset is not an easy task. Thus, we propose a methodology to guide the acquisition of a relevant underwater calibration dataset. This process is intended to provide feedback in near real-time to the operator to guide the acquisition and stop it when a sufficient number of relevant calibration images have been reached. To perform this, pattern detection must be optimized both in time and success rate. We propose three variations of optimized detection algorithms, each of which takes into account different hardware capabilities. We present the results obtained on a homemade database composed of 60,000 images taken both in pools and at sea.