The aim of this project is to design and train a novel Artificial Neural Network (ANN): LiDomNet. The goal of this network is to recover odometry (i.e., a quaternion describing rotation and a traslation vector) corresponding to movement between two consecutive 3D point-clouds. For this purpose, we have used public KITTI dataset which provides, among other data, measurements from a 3D LiDar with 64 beams, attached on the top of a moving car. In addition, they provide real data of car’s movement gathered with an Inertial Measurement Unit (IMU). This project is yet active.

The SIAR project will develop a fully autonomous ground robot able to autonomously navigate and inspect the sewage system with a minimal human intervention, and with the possibility of manually controlling the vehicle or the sensor payload when required. The project uses as starting point IDMind’s robot platform RaposaNG. A new robot will be built based on this know-how, with the following key steps beyond the state of the art required to properly address the challenge: a robust IP67 robot frame designed to work in the hardest environmental conditions with increased power autonomy and flexible inspection capabilities; robust and increased communication capabilities; onboard autonomous navigation and inspection capabilities; usability and cost effectiveness of the developed solution. UPO leads the navigation tasks on the project

Micro unmanned Aerial Vehicles (MAVs) may open up a new plethora of applications for aerial robotics, both in indoor and outdoors scenarios. However, the limited payload of these vehicles limits the sensors and processing power that can be carried by MAVs, and, thus, the level of autonomy they can achieve without relying on external sensing and processing. Flying insects, like Drosophila, on the other hand, can carry out impressive maneuvers with a relatively small neural system. This project will explore the biological fundamentals of Drosophilas ocelli sensory-motor system, one of the mechanisms most likely related to fly stabilization, and the possibility to derive new sensing and navigation systems for MAVs from it.The project will do it by a complete reverse engineering of the ocelli system, estimating the structure and functionality of its neural processing network, and then modeling it through the interaction of biological and engineering research. Novel genetic-based neural tracing methods will be employed to extract the topology of the neural network, and behavioral experiments will be devised to determine the functionalities of specific neurons. The findings will be used to derive a model, and this model will be used to characterize the relevant aspects from the point of view of estimation and control. The model will also serve to determine the adaptation of this sensory-motor system to current MAV platforms, and to design of a proof of concept sensing and navigation system.