High Accuracy Motion Detection Algorithm via ISM Band FMCW Radar

The conventional frequency modulated continuous wave (FMCW) radar accuracy range detection algorithm is based on the frequency estimation and additional phase evaluation which contains Fourier transform and frequency refining analysis in each chirp, so it has the disadvantages of being computationally expensive, and not being suitable for real-time motion measurement. In addition, if there are other objects near the target, the spectra of the clutter and the target will be adjacent and affect each other, making it more challenging to estimate the frequency of the target. In this paper, the analytical expression of the Fourier transform of the beat signal is presented and it can be seen that spectrum leakage makes the phase of Fourier transform no longer consistent with the real phase of signal. The change regularities of real and imaginary parts of Fourier transform are studied, and the corrected phase of ellipse approximation is given in the industrial, scientific, and medical (ISM) band. Accurate displacement can be obtained by accurate phase. The algorithm can filter the direct current (DC) offset which is mainly caused by stationary objects. The performance of the algorithm is evaluated by a radar system whose center frequency is 24.075 GHz and the bandwidth is 0.15 GHz; the measurement accuracy of displacement is 0.087 mm and the accuracy of distance is 0.043 m.

Real-Time Detection of Non-Stationary Objects Using Intensity Data in Automotive LiDAR SLAM

This article aims at demonstrating the feasibility of modern deep learning techniques for the real-time detection of non-stationary objects in point clouds obtained from 3-D light detecting and ranging (LiDAR) sensors. The motion segmentation task is considered in the application context of automotive Simultaneous Localization and Mapping (SLAM), where we often need to distinguish between the static parts of the environment with respect to which we localize the vehicle, and non-stationary objects that should not be included in the map for localization. Non-stationary objects do not provide repeatable readouts, because they can be in motion, like vehicles and pedestrians, or because they do not have a rigid, stable surface, like trees and lawns. The proposed approach exploits images synthesized from the received intensity data yielded by the modern LiDARs along with the usual range measurements. We demonstrate that non-stationary objects can be detected using neural network models trained with 2-D grayscale images in the supervised or unsupervised training process. This concept makes it possible to alleviate the lack of large datasets of 3-D laser scans with point-wise annotations for non-stationary objects. The point clouds are filtered using the corresponding intensity images with labeled pixels. Finally, we demonstrate that the detection of non-stationary objects using our approach improves the localization results and map consistency in a laser-based SLAM system.

Collision Avoidance on Unmanned Aerial Vehicles Using Neural Network Pipelines and Flow Clustering Techniques

Unmanned Autonomous Vehicles (UAV), while not a recent invention, have recently acquired a prominent position in many industries, and they are increasingly used not only by avid customers, but also in high-demand technical use-cases, and will have a significant societal effect in the coming years. However, the use of UAVs is fraught with significant safety threats, such as collisions with dynamic obstacles (other UAVs, birds, or randomly thrown objects). This research focuses on a safety problem that is often overlooked due to a lack of technology and solutions to address it: collisions with non-stationary objects. A novel approach is described that employs deep learning techniques to solve the computationally intensive problem of real-time collision avoidance with dynamic objects using off-the-shelf commercial vision sensors. The suggested approach’s viability was corroborated by multiple experiments, firstly in simulation, and afterward in a concrete real-world case, that consists of dodging a thrown ball. A novel video dataset was created and made available for this purpose, and transfer learning was also tested, with positive results.

Multispectral optoelectronic device for controlling an autonomous mobile platform

The paper substantiates the use of multispectral optoelectronic sensors intended to solve the problem of improving the positioning accuracy of autonomous mobile platforms. A mathematical model of the developed device operation has been suggested in the paper. Its distinctive feature is the cooperative processing of signals obtained from sensors operating in ultraviolet, visible, and infrared ranges and lidar. It reduces the computational complexity of detecting dynamic and stationary objects within the field of view of the device by processing data on the diffuse reflectivity of materials. The paper presents the functional organization of a multispectral optoelectronic device that makes it possible to detect and classify working scene objects with less time spending as compared to analogs. In the course of experimental research, the validity of the mathematical model was evaluated and there were obtained empirical data by means of the proposed hardware and software test stand. The accuracy evaluation of the detected object, at a distance of up to 100m inclusive, is within 0.95. At a distance of more than 100 m, it decreases. This is due to the operating range of a lidar. Error in determining spatial coordinates is of exponential character and it also increases sharply at a distance close to 100 m.

Sparingly developed tourist places on Lake Baikal

The paper presents a sparing approach to the tourist and recreation development on Lake Baikal. This is conditioned by several factors: lack of sufficient theoretical basis on the regional architecture, the participation of the government, federal programs and projects on the development of tourism as an important socioeconomic sphere; high pollution and the constantly deteriorating ecosystem of the lake.The aim of this work is to develop a concept (model) of sparingly developed tourist and recreation places on Lake Baikal, a choice of types and principles of placing objects and their main architectural and functional properties. The paper presents the analysis and synthesis of the data from the Federal Target Programs, literature, methodologies, regulatory and design materials on the problem.As a result, the proposed model of sparingly developed tourist and recreation places includes intensive, normal, moderate and promising development. The main principles for the object placement are based on the mobility of stationary objects from the coastline, creation of research centers responsible for constant ecosystem monitoring, and traditional and fishing centers. These solutions allow creating the functioning system of the tourist cluster, interesting tourist routes and their architectural and functional diversity.

Monocular Visual-Inertial Navigation for Dynamic Environment

Simultaneous localization and mapping (SLAM) systems have been generally limited to static environments. Moving objects considerably reduce the location accuracy of SLAM systems, rendering them unsuitable for several applications. Using a combined vision camera and inertial measurement unit (IMU) to separate moving and static objects in dynamic scenes, we improve the location accuracy and adaptability of SLAM systems in these scenes. We develop a moving object-matched feature points elimination algorithm that uses IMU data to eliminate matches on moving objects but retains them on stationary objects. Moreover, we develop a second algorithm to validate the IMU data to avoid erroneous data from influencing image feature points matching. We test the new algorithms with public datasets and in a real-world experiment. In terms of the root mean square error of the location absolute pose error, the proposed method exhibited higher positioning accuracy for the public datasets than the traditional algorithms. Compared with the closed-loop errors obtained by OKVIS-mono and VINS-mono, those obtained in the practical experiment were lower by 50.17% and 56.91%, respectively. Thus, the proposed method eliminates the matching points on moving objects effectively and achieves feature point matching results that are realistic.

The role of environmental factors in the realization of the epidemic process on ascariasis

Introduction. Ascariasis is one of the most common parasitic diseases that infect about 1.5 million people in the world every year. In Russia, from 40 to 60 thousand cases are registered annually. To make effective management decisions on sanitary-epidemiological and preventive measures, the search for environmental factors that potentially affect the epidemic process of ascariasis in the Vladimir region was carried out. Materials and methods. The study analyzed archived statistical data on the incidence of ascariasis in the population, the state of the environment (air, water, and soil pollution), the socio-economic situation (unemployment rate, average salary, number of doctors and nurses, housing provision), as well as climate indices (average monthly temperature, number of days in a month with precipitation, humidity and snow cover) in the region. Statistical data processing and correlation and regression analysis were performed using the Statistica software. The maps were built and edited using the ArcView 3.1 GIS program and the standard Paint computer program. Results. The incidence of ascariasis in the region is characterized by significant diversity. Statistically reliable correlations of morbidity with environmental indices were obtained, and a mathematical equation was constructed using linear regression to predict the level of morbidity in the region. Conclusion. In the epidemiology of ascariasis in the Vladimir region, a leading role retains climatic, environmental indices determining the possibility of developing eggs of ascarids in the environment to the infective stage. A regional factor that affects the epidemiological process (indirectly through the suppression of the protective functions of the child’s body and reducing the development time of Ascaris eggs) is air pollution by stationary objects. The data obtained can assess the risk of infection with ascariasis to ensure biological safety in the region.