Feasibility study of a multispectral camera with automatic processing onboard a 27U satellite using Model Based Space System Engineering

The paper discusses an experience in using SysML and the TTool software for the feasibility study of a novel multispectral camera for agricultural monitoring. Innovation lies in both automatic image processing onboard and mission control capabilities designed to comply with a 27U microsatellite. In addition to the mission accomplishment control, this innovative payload is capable of sending processed data directly to farms, critically reducing the delay between image making and its use in the field. This paper shows how MBSE and SysML may comply with phases 0 and A of a space project

Crop Height and Plot Estimation for Phenotyping from Unmanned Aerial Vehicles using 3D LiDAR

We present techniques to measure crop heights using a 3D Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV). Knowing the height of plants is crucial to monitor their overall health and growth cycles, especially for high-throughput plant phenotyping. We present a methodology for extracting plant heights from 3D LiDAR point clouds, specifically focusing on plot-based phenotyping environments. We also present a toolchain that can be used to create phenotyping farms for use in Gazebo simulations. The tool creates a randomized farm with realistic 3D plant and terrain models. We conducted a series of simulations and hardware experiments in controlled and natural settings. Our algorithm was able to estimate the plant heights in a field with 112 plots with a root mean square error (RMSE) of 6.1 cm. This is the first such dataset for 3D LiDAR from an airborne robot over a wheat field. The developed simulation toolchain, algorithmic implementation, and datasets can be found on the GitHub repository located at https://github.com/hsd1121/PointCloudProcessing.Comment: 8 pages, 10 figures, 1 table, Accepted to IROS 202

RAPID MAPPING FOR BUILT HERITAGE AT RISK USING LOW-COST AND COTS SENSORS. A TEST IN THE DUOMO VECCHIO OF SAN SEVERINO MARCHE

In the last years, the researchers in the field of Geomatics have focused their attention in the experimentation and validation of new methodologies and techniques, stressing especially the potential of low-cost and COTS (Commercial Off The Shelf) solutions and sensors. In particular, these tools have been used with purposes of rapid mapping in different contexts (ranging from the construction industry, environmental monitoring, mining activities, etc.). The Built Heritage, due to its intrinsic nature of endangered artefact, can largely benefit from the technological and methodological innovations in this research field. The contribute presented in this paper will highlight these main topics: the rapid mapping of the Built Heritage (in particular the one subjected to different types of risk) using low-cost and COTS solutions. Different sensors and techniques were chosen to be evaluated on a specific test site: the Duomo Vecchio of San Severino Marche (MC - Italy), that was partially affected by the earthquake swarm that hit the area of Central Italy starting from the 24th of August 2016. One of the main aims of this work is to demonstrate how low-cost and COTS sensors can contribute to the documentation of the Built Heritage for its safeguard, for damage assessment in case of disastrous events and operations of restoration and preservation

GATSBI: An Online GTSP-Based Algorithm for Targeted Surface Bridge Inspection

We study the problem of visually inspecting the surface of a bridge using an Unmanned Aerial Vehicle (UAV) for defects. We do not assume that the geometric model of the bridge is known. The UAV is equipped with a LiDAR and RGB sensor that is used to build a 3D semantic map of the environment. Our planner, termed GATSBI, plans in an online fashion a path that is targeted towards inspecting all points on the surface of the bridge. The input to GATSBI consists of a 3D occupancy grid map of the part of the environment seen by the UAV so far. We use semantic segmentation to segment the voxels into those that are part of the bridge and the surroundings. Inspecting a bridge voxel requires the UAV to take images from a desired viewing angle and distance. We then create a Generalized Traveling Salesperson Problem (GTSP) instance to cluster candidate viewpoints for inspecting the bridge voxels and use an off-the-shelf GTSP solver to find the optimal path for the given instance. As more parts of the environment are seen, we replan the path. We evaluate the performance of our algorithm through high-fidelity simulations conducted in Gazebo. We compare the performance of this algorithm with a frontier exploration algorithm. Our evaluation reveals that targeting the inspection to only the segmented bridge voxels and planning carefully using a GTSP solver leads to more efficient inspection than the baseline algorithms.Comment: 8 pages, 16 figure