Functional description of a terrestrial-aerial robot to detect and mark dangerous areas

There is an urgent need for a quick and effective survey of areas hit by accidents, that result in contamination with chemical, radioactive or explosive materials. Hence the need for mobile robots, which should be able to perform tasks in different environments for a long period of time, so that they can detect hazardous materials, identify their sources and create maps of their distribution. As a result, we can limit the spread of dangerous materials to other areas and reduce their harmful effects on the environment and the health of people. In this paper, we present a functional description of a robot capable of terrestrial-aerial movement in order to inspect areas and mark the contaminated parts. Here, we focus on the following topics: drone basic components, communications between the robot and the workstation, robot positioning, and detection of chemical contamination. During that we present a review of many important contributions in order to show the latest developments and try to explore future research directions

Designing a machine vision system for a mobile robot to detect and mark dangerous areas

There is no doubt that machine vision systems offer many benefits in many applications, as they improve the ability of machines to adapt and learn. When implementing a new application it is necessary to design a vision system that matches the requirements of the application, as there is a wide range of parameters that must be considered during the design. Our goal in this paper is to learn about these different parameters and define the different requirements for designing a machine vision system for a mobile robot, whose task is to examine different environments autonomously, detect hazardous materials, and mark high-risk areas, in various weather conditions and around the clock

A method to create real-like point clouds for 3D object classification

There are a large number of publicly available datasets of 3D data, they generally suffer from some drawbacks, such as small number of data samples, and class imbalance. Data augmentation is a set of techniques that aim to increase the size of datasets and solve such defects, and hence to overcome the problem of overfitting when training a classifier. In this paper, we propose a method to create new synthesized data by converting complete meshes into occluded 3D point clouds similar to those in real-world datasets. The proposed method involves two main steps, the first one is hidden surface removal (HSR), where the occluded parts of objects surfaces from the viewpoint of a camera are deleted. A low-complexity method has been proposed to implement HSR based on occupancy grids. The second step is a random sampling of the detected visible surfaces. The proposed two-step method is applied to a subset of ModelNet40 dataset to create a new dataset, which is then used to train and test three different deep-learning classifiers (VoxNet, PointNet, and 3DmFV). We studied classifiers performance as a function of the camera elevation angle. We also conducted another experiment to show how the newly generated data samples can improve the classification performance when they are combined with the original data during training process. Simulation results show that the proposed method enables us to create a large number of new data samples with a small size needed for storage. Results also show that the performance of classifiers is highly dependent on the elevation angle of the camera. In addition, there may exist some angles where performance degrades significantly. Furthermore, data augmentation using our created data improves the performance of classifiers not only when they are tested on the original data, but also on real data