Sensor System for Rescue Robots

A majority of rescue worker fatalities are a result of on-scene responses. Existing technologies help assist the first responders in scenarios of no light, and there even exist robots that can navigate radioactive areas. However, none are able to be both quickly deployable and enter hard to reach or unsafe areas in an emergency event such as an earthquake or storm that damages a structure. In this project we created a sensor platform system to augment existing robotic solutions so that rescue workers can search for people in danger while avoiding preventable injury or death and saving time and resources. Our results showed that we were able to map out a 2D map of the room with updates for robot motion on a display while also showing a live thermal image in front of the system. The system is also capable of taking a digital picture from a triggering event and then displaying it on the computer screen. We discovered that data transfer plays a huge role in making different programs like Arduino and Processing interact with each other. Consequently, this needs to be accounted for when improving our project. In particular our project is wired right now but should deliver data wirelessly to be of any practical use. Furthermore, we dipped our feet into SLAM technologies and if our project were to become autonomous, more research into the algorithms would make this autonomy feasible

Determination of forest road surface roughness by kinect depth imaging

Roughness is a dynamic property of the gravel road surface that affects safety, ride comfort as well as vehicle tyre life and maintenance costs. A rapid survey of gravel road condition is fundamental for an effective maintenance planning and definition of the intervention priorities. Different non-contact techniques such as laser scanning, ultrasonic sensors and photogrammetry have recently been proposed to reconstruct three-dimensional topography of road surface and allow extraction of roughness metrics. The application of Microsoft Kinect\u2122 depth camera is proposed and discussed here for collection of 3D data sets from gravel roads, to be implemented in order to allow quantification of surface roughness. The objectives are to: i) verify the applicability of the Kinect sensor for characterization of different forest roads, ii) identify the appropriateness and potential of different roughness parameters and iii) analyse the correlation with vibrations recoded by 3-axis accelerometers installed on different vehicles. The test took advantage of the implementation of the Kinect depth camera for surface roughness determination of 4 different forest gravel roads and one well-maintained asphalt road as reference. Different vehicles (mountain bike, off-road motorcycle, ATV vehicle, 4WD car and compact crossover) were included in the experiment in order to verify the vibration intensity when travelling on different road surface conditions. Correlations between the extracted roughness parameters and vibration levels of the tested vehicles were then verified. Coefficients of determination of between 0.76 and 0.97 were detected between average surface roughness and standard deviation of relative accelerations, with higher values in the case of lighter vehicles

Älykkäät huonejärjestelmät perusparannetuissa koulurakennuksissa

Smart technologies in buildings can improve user satisfaction, energy efficiency and the performance of technical systems. Demand-based ventilation and heating solutions are used to achieve great indoor environment quality energy efficiently. The European Commission has introduced a Smart Readiness Indicator in the new Energy performance of buildings directive, which aims at proving the added value that smart technologies bring to the building owners, users and tenants. The objectives of the thesis are to evaluate how ICT-technology and services can be used in buildings through case examples and to integrate different systems to co-operate including building automation, HVAC and a mobile application. The measurements were conducted in seven rooms in Aalto University’s Undergraduate center. The main improvements were: the monitoring and controllability of the variable air volume ventilation- and water radiator heating-system through Aalto space – mobile app, occupancy measurements and the collection of user satisfaction feedback. The VAV-ventilation system in the case rooms worked as designed. The CO2 concentration varied with each room, but the temperatures were nearly identical and stable. Three different control strategies for the ventilation were tested, where the combination of both temperature and CO2 concentration proved to be the best solution. The ratio between exhaust and supply air flows varied from room to room, best being 100% and worst 60%. This difference could be seen in the results of the pressure differences over the building envelope. This measurement was used to assess the performance of the ventilation system. All rooms were underpressured and there was a clear difference between day and night time pressures difference over the building envelope. During the night, the air handling unit of the zone serving the case rooms was not operating. Still during the nights, some general exhaust fan operating causing the greater underpressure. Room occupancy was measured with image- and CO2 concentration-based methods. Image-based methods provided varying results. The Kinect sensor had problems in identifying people, but the AXIS-3045 worked well with 95% accuracy. CO2 concentration-based method was accurate to one person 66% of the time and 89% accurate in identifying if the room is occupied or not. The error is caused by the latency of change of the concentration in the rooms. Also, the CO2 generation rates by humans and the accuracy of the supply and exhaust air flows can cause errors. User satisfaction in the rooms was measured with a paper survey and through Aalto space – mobile app. The results indicate that people are quite satisfied with the rooms as through the paper survey 71% answered +/- 1 on the PMV scale and through Aalto Space 84% answered either four or five stars out of five. Nearly half rated the indoor temperature as slightly cool/cool or cold. The indoor temperature was considered to be acceptable by 69% and the air quality by 79% of the respondents.Rakennusten älykkäät teknologiat parantavat käyttäjätyytyväisyyttä, energiatehokkuutta sekä rakennusten elinikää. Tarpeenmukaisen ilmanvaihdon ja lämmityksen ratkaisuilla saavutetaan energiatehokkaasti korkeatasoinen sisäilmaston laatu. Euroopan komissio on julkaissut uuden Smart Readiness indikaattorin, jonka tarkoituksena on korostaa älykkäiden teknologioiden tuoma lisäarvo rakennusten omistajille, käyttäjille sekä asukkaille. Tämän työn tavoitteena on arvioida miten ICT-teknologiaa ja palveluita voidaan käyttää rakennuksissa esimerkkitapausten avulla sekä integroida eri taloteknisiä ja muita järjestelmiä, kuten rakennusautomaation ja LVI:n sekä mobiilisovelluksen yhteen. Mittaukset toteutettiin seitsemässä huoneessa Aalto-yliopiston Kandidaattikeskuksessa. Tärkeimmät parannukset olivat: muuttuvan ilmavirtasääteisen ilmanvaihdon sekä vesiradiaattorijärjestelmän seuranta sekä ohjaus Aalto Space-mobiilisovelluksella, huoneiden käyttöasteen mittaus sekä käyttäjätyytyväisyys palautteen kerääminen. Muuttuva ilmavirtasääteinen ilmanvaihto toimi huoneissa kuten se oli suunniteltu. Sisäilmaolosuhteet vaihtelivat huoneiden välillä hiilidioksidipitoisuuden osalta, mutta lämpötila oli lähes identtinen jokaisessa huoneessa. Huoneissa testattiin kolmea eri ilmanvaihdonohjausstrategiaa, joista lämpötilan ja hiilidioksidipitoisuuden yhteisohjaus osoittautui parhaaksi ratkaisuksi. Myös tulo- ja poistoilmavirtojen suhde vaihteli huoneissa. Muutamissa huoneissa ilmavirrat olivat noin 100 % tasapainossa ja joissakin huoneissa suhde oli jopa 60 %. Tämä ero näkyi esimerkiksi huoneiden paine-eroissa rakennuksen vaipan yli. Paine-ero mittauksia tehtiin arvioidakseen ilmanvaihtojärjestelmän toimivuutta. Kaikki huoneet olivat alipaineisia. Alipaine oli selvästi suurempi öisin kuin päivisin. Tämä muutos johtuu siitä, että huoneiden ilmanvaihtokone on öisin pois päältä, mutta rakennuksessa on muita poistoilmanvaihtokoneita päällä. Käyttäjätyytyväisyyttä mitattiin kuudella kysymyksellä paperisena sekä Aalto Space -mobiilisovelluksen avulla. Tulokset osoittavat, että ihmiset ovat melko tyytyväisiä huoneiden sisäilmastoon, sillä paperikyselyiden kautta 71 % vastasi +/- 1 PMV-asteikolla ja Aalto Spacen kautta 84 % vastasi joko neljä tai viisi tähteä viidestä. Lähes puolet vastaajista kertoi sisälämpötilan olevan hieman viileä, viileä tai kylmä. Hyväksyttävänä sisälämpötilaa piti 69 % ja ilmanlaatua 79 % vastaajista

Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras

Color-depth cameras (RGB-D cameras) have become the primary sensors in most robotics systems, from service robotics to industrial robotics applications. Typical consumer-grade RGB-D cameras are provided with a coarse intrinsic and extrinsic calibration that generally does not meet the accuracy requirements needed by many robotics applications (e.g., highly accurate 3D environment reconstruction and mapping, high precision object recognition and localization, ...). In this paper, we propose a human-friendly, reliable and accurate calibration framework that enables to easily estimate both the intrinsic and extrinsic parameters of a general color-depth sensor couple. Our approach is based on a novel two components error model. This model unifies the error sources of RGB-D pairs based on different technologies, such as structured-light 3D cameras and time-of-flight cameras. Our method provides some important advantages compared to other state-of-the-art systems: it is general (i.e., well suited for different types of sensors), based on an easy and stable calibration protocol, provides a greater calibration accuracy, and has been implemented within the ROS robotics framework. We report detailed experimental validations and performance comparisons to support our statements

Kinect Range Sensing: Structured-Light versus Time-of-Flight Kinect

Recently, the new Kinect One has been issued by Microsoft, providing the next generation of real-time range sensing devices based on the Time-of-Flight (ToF) principle. As the first Kinect version was using a structured light approach, one would expect various differences in the characteristics of the range data delivered by both devices. This paper presents a detailed and in-depth comparison between both devices. In order to conduct the comparison, we propose a framework of seven different experimental setups, which is a generic basis for evaluating range cameras such as Kinect. The experiments have been designed with the goal to capture individual effects of the Kinect devices as isolatedly as possible and in a way, that they can also be adopted, in order to apply them to any other range sensing device. The overall goal of this paper is to provide a solid insight into the pros and cons of either device. Thus, scientists that are interested in using Kinect range sensing cameras in their specific application scenario can directly assess the expected, specific benefits and potential problem of either device.Comment: 58 pages, 23 figures. Accepted for publication in Computer Vision and Image Understanding (CVIU

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

New strategies for row-crop management based on cost-effective remote sensors

Agricultural technology can be an excellent antidote to resource scarcity. Its growth has led to the extensive study of spatial and temporal in-field variability. The challenge of accurate management has been addressed in recent years through the use of accurate high-cost measurement instruments by researchers. However, low rates of technological adoption by farmers motivate the development of alternative technologies based on affordable sensors, in order to improve the sustainability of agricultural biosystems. This doctoral thesis has as main objective the development and evaluation of systems based on affordable sensors, in order to address two of the main aspects affecting the producers: the need of an accurate plant water status characterization to perform a proper irrigation management and the precise weed control. To address the first objective, two data acquisition methodologies based on aerial platforms have been developed, seeking to compare the use of infrared thermometry and thermal imaging to determine the water status of two most relevant row-crops in the region, sugar beet and super high-density olive orchards. From the data obtained, the use of an airborne low-cost infrared sensor to determine the canopy temperature has been validated. Also the reliability of sugar beet canopy temperature as an indicator its of water status has been confirmed. The empirical development of the Crop Water Stress Index (CWSI) has also been carried out from aerial thermal imaging combined with infrared temperature sensors and ground measurements of factors such as water potential or stomatal conductance, validating its usefulness as an indicator of water status in super high-density olive orchards. To contribute to the development of precise weed control systems, a system for detecting tomato plants and measuring the space between them has been developed, aiming to perform intra-row treatments in a localized and precise way. To this end, low cost optical sensors have been used and compared with a commercial LiDAR laser scanner. Correct detection results close to 95% show that the implementation of these sensors can lead to promising advances in the automation of weed control. The micro-level field data collected from the evaluated affordable sensors can help farmers to target operations precisely before plant stress sets in or weeds infestation occurs, paving the path to increase the adoption of Precision Agriculture techniques