Yhtäaikainen paikannus ja puuston kartoitus 2D- ja 3D- laserskannereilla

Yhtäaikainen paikannus ja puuston kartoitus 2D- ja 3D- laserskannereilla esittää tavan mitata ja kartoittaa metsän puut. Työssä mittaukset tehtiin paikallisesti metsässä käyttäen kaksi ja kolmiulotteisia laserskannereita liikkeestä mitaten. Työ esittää menetelmän puumaiset kohteiden tunnistamiseen mittalaitteiden tuottamasta pisteparvesta reaaliajassa. Työssä mittaus ja kartoitusalgoritmit sovitetaan erityistesti metsän kartoitusta ja puuston mittausta varten. Diplomityössä yhtäaikaisen paikoituksen ja kartoituksen ongelmaa lähdetään ratkaisemaan mittauslaitteen mahdollisimman tarkan paikan ja asennon estimoinnista metsäolosuhteissa. Mittauslaitteiston paikkaa mitataan laserodometrialla, jossa perättäisiä laserkeilauksia verrataan toisiinsa ja näiden väliltä tunnistetaan liike suhteessa ympäristöön. Työssä kehitetään uusia heuristisia paikannusmetodeja metsäympäristöön. Työ esittelee uuden tavan käyttää ristikorrelaatioita laserodometriassa ja näyttää miten gyro- ja kiihtyvyysantureita voidaan käyttää odometriatiedon parantamiseen. Diplomityö esittää piirrepohjaisen puukartan, jonne 2D- ja 3D-laseretäisyysmittauksista lasketut piirteet lisätään. Karttaa päivitetään jatkuvasti ja uusinta kartan tietoa käytetään mitatun paikkatiedon parantamiseen. Samoin kaikkia kerättyjä mittauksia käytetään tilastollisesti parantamaan aikaisemmin eri korkeuksilta laskettuja puurunkojen läpimittaestimaatteja. Lopputuloksena saatu kartta on melko tarkka. Kartoituksessa puun läpimitan tarkkuus on muutamia senttimetrejä ja puun paikan tarkkuus muuta kymmenen senttimetriä. Mittauslaitteen paikan arvioidaan olevan tarkempi kuin puiden, koska suurta määrää kartoitettuja puita käytetään mittauslaitteen paikan ja asennon sovittamiseen. Suhteellinen kartta on kiinnitetty globaaliin koordinaatistoon GPS mittalaitteen avulla

Feature Based Modeling and Mapping of Tree Trunks and Natural Terrain Using 3D Laser Scanner Measurement System

This paper presents a novel approach to measure tree trunks and to model the ground using a 3D laser scanner. The 3D scanner, self-build using two 2D Sick scanners on a rotating base, measures each scan line approximately at 45° angle towards the ground and the trees. Single scan lines are segmented to find ground and tree returns. 3D point clouds from the surrounding forest are recorded while the measuring vehicle is moving. Sequential scan lines are joined together as the pose changes are reduced from the older buffered measurements. Laser odometry and inertial measurements are used to measure the pose changes. The ground is modeled by fitting a 1m grid to 3D point cloud extracted using a ground return detector. Tree trunks are searched from the 3D point cloud using a histogram approach to segment measurements into separate point clouds for each tree trunk. Tree trunks are modeled using ten circle features one on the other using the extracted point cloud. Instead of using the whole point cloud, mapping is done only for the extracted features and the travelled path to save computation time. Our method can detect nearly all tree trunks and measure them on short ranges of less than 8m with errors less than 4cm in diameter.Peer reviewe

Autonomisten metsäkoneiden koneaistijärjestelmät

A prerequisite for increasing the autonomy of forest machinery is to provide robots with digital situational awareness, including a representation of the surrounding environment and the robot's own state in it. Therefore, this article-based dissertation proposes perception systems for autonomous or semi-autonomous forest machinery as a summary of seven publications. The work consists of several perception methods using machine vision, lidar, inertial sensors, and positioning sensors. The sensors are used together by means of probabilistic sensor fusion. Semi-autonomy is interpreted as a useful intermediary step, situated between current mechanized solutions and full autonomy, to assist the operator. In this work, the perception of the robot's self is achieved through estimation of its orientation and position in the world, the posture of its crane, and the pose of the attached tool. The view around the forest machine is produced with a rotating lidar, which provides approximately equal-density 3D measurements in all directions. Furthermore, a machine vision camera is used for detecting young trees among other vegetation, and sensor fusion of an actuated lidar and machine vision camera is utilized for detection and classification of tree species. In addition, in an operator-controlled semi-autonomous system, the operator requires a functional view of the data around the robot. To achieve this, the thesis proposes the use of an augmented reality interface, which requires measuring the pose of the operator's head-mounted display in the forest machine cabin. Here, this work adopts a sensor fusion solution for a head-mounted camera and inertial sensors. In order to increase the level of automation and productivity of forest machines, the work focuses on scientifically novel solutions that are also adaptable for industrial use in forest machinery. Therefore, all the proposed perception methods seek to address a real existing problem within current forest machinery. All the proposed solutions are implemented in a prototype forest machine and field tested in a forest. The proposed methods include posture measurement of a forestry crane, positioning of a freely hanging forestry crane attachment, attitude estimation of an all-terrain vehicle, positioning a head mounted camera in a forest machine cabin, detection of young trees for point cleaning, classification of tree species, and measurement of surrounding tree stems and the ground surface underneath.Metsäkoneiden autonomia-asteen kasvattaminen edellyttää, että robotilla on digitaalinen tilannetieto sekä ympäristöstä että robotin omasta toiminnasta. Tämän saavuttamiseksi työssä on kehitetty autonomisen tai puoliautonomisen metsäkoneen koneaistijärjestelmiä, jotka hyödyntävät konenäkö-, laserkeilaus- ja inertia-antureita sekä paikannusantureita. Työ liittää yhteen seitsemässä artikkelissa toteutetut havainnointimenetelmät, joissa useiden anturien mittauksia yhdistetään sensorifuusiomenetelmillä. Työssä puoliautonomialla tarkoitetaan hyödyllisiä kuljettajaa avustavia välivaiheita nykyisten mekanisoitujen ratkaisujen ja täyden autonomian välillä. Työssä esitettävissä autonomisen metsäkoneen koneaistijärjestelmissä koneen omaa toimintaa havainnoidaan estimoimalla koneen asentoa ja sijaintia, nosturin asentoa sekä siihen liitetyn työkalun asentoa suhteessa ympäristöön. Yleisnäkymä metsäkoneen ympärille toteutetaan pyörivällä laserkeilaimella, joka tuottaa lähes vakiotiheyksisiä 3D-mittauksia jokasuuntaisesti koneen ympäristöstä. Nuoret puut tunnistetaan muun kasvillisuuden joukosta käyttäen konenäkökameraa. Lisäksi puiden tunnistamisessa ja puulajien luokittelussa käytetään konenäkökameraa ja laserkeilainta yhdessä sensorifuusioratkaisun avulla. Lisäksi kuljettajan ohjaamassa puoliautonomisessa järjestelmässä kuljettaja tarvitsee toimivan tavan ymmärtää koneen tuottaman mallin ympäristöstä. Työssä tämä ehdotetaan toteutettavaksi lisätyn todellisuuden käyttöliittymän avulla, joka edellyttää metsäkoneen ohjaamossa istuvan kuljettajan lisätyn todellisuuden lasien paikan ja asennon mittaamista. Työssä se toteutetaan kypärään asennetun kameran ja inertia-anturien sensorifuusiona. Jotta metsäkoneiden automatisaatiotasoa ja tuottavuutta voidaan lisätä, työssä keskitytään uusiin tieteellisiin ratkaisuihin, jotka soveltuvat teolliseen käyttöön metsäkoneissa. Kaikki esitetyt koneaistijärjestelmät pyrkivät vastaamaan todelliseen olemassa olevaan tarpeeseen nykyisten metsäkoneiden käytössä. Siksi kaikki menetelmät on implementoitu prototyyppimetsäkoneisiin ja tulokset on testattu metsäympäristössä. Työssä esitetyt menetelmät mahdollistavat metsäkoneen nosturin, vapaasti riippuvan työkalun ja ajoneuvon asennon estimoinnin, lisätyn todellisuuden lasien asennon mittaamisen metsäkoneen ohjaamossa, nuorten puiden havaitsemisen reikäperkauksessa, ympäröivien puiden puulajien tunnistuksen, sekä puun runkojen ja maanpinnan mittauksen

Real-time Detection of Young Spruce Using Color and Texture Features on an Autonomous Forest Machine

Forest machines are manually operated machines that are efficient when operated by a professional. Point cleaning is a silvicultural task in which weeds are removed around a young spruce tree. To automate point cleaning, machine vision methods are used for identifying spruce trees. A texture analysis method based on the Radon and wavelet transforms is implemented for the task. Real-time GPU implementation of algorithms is programmed using CUDA framework. Compared to a single thread CPU implementation, our GPU implementation is between 18 to 80 times faster depending on the size of image blocks used. Color information is used in addition of texture and a location estimate of the tree is extracted from the detection result. The developed spruce detection system is used as a part of an autonomous point cleaning machine. To control the system, an integrated user interface is presented. It allows the operator to control, monitor and train the system online.Peer reviewe

Sway Estimation Using Inertial Measurement Units for Cranes with a Rotating Tool

Cranes have often a freely hanging load or tool that starts easily swaying. Anti-sway control requires that the angles and angular velocities of the swinging object are measured. Some cranes can also rotate the tool with a hydraulic motor, and in many cases this rotator angle should also be known. Instrumenting all three axes, two swaying and one rotating axis, with traditional rotary encoders can be challenging. We propose an extended Kalman filter based system using two inertial measurement units. This system can measure the swaying in both directions and estimate the rotator angle. Computer vision system is used as reference. The initial results show that the error is approximately 5 degrees in the rotator angle and 2 degrees in the sway angles. The observer runs at 100 Hz on an embedded microcontroller.Peer reviewe

Augmented Reality in Forest Machine Cabin

Augmented reality human machine interface is demonstrated in the cabin of a forest machine outdoors for the first time in real time. In this work, we propose a system setup and a real-time capable algorithm to augment the operator’s visual field with measurements from the forest machine and its environment. In the demonstration, an instrumented forestry crane and a lidar are used to model the pose of the crane and its surroundings. In our approach, a camera and an inertial measurement unit are used to estimate the pose of the operator’s head in difficult lighting conditions with the help of planar markers placed on the cabin structures. Using the estimate, a point cloud and a crane model are superimposed on the video feed to form an augmented reality view. Our system is tested to work outdoors using a forest machine research platform in real time with encouraging initial results.Peer reviewe

Detection and species classification of young trees using machine perception for a semi-autonomous forest machine

An approach to automatically detect and classify young spruce and birch trees in forest environment is presented. The method could be used in autonomous or semi-autonomous forest machines during tending operations. Detection is done by segmenting laser range images formed by a rotating laser scanner. Classification is done with a two-class Naive Bayes classifier based on image texture features. Multiple combinations of 99 features were tested and the best classifier included eight features from the co-occurrence matrix, local binary patterns, statistical geometrical features and Gabor filter. 79% of spruces and birches in the testing material were detected and 74% of these were correctly classified. Results suggest that the approach is suitable but there are still some challenges in each of the processing steps. Iteration between segmentation and classification is needed to increase reliability.Peer reviewe

ISO 11783 Compliant Forest Crane As a Platform for Automatic Control

ISO 11783 is a communication standard for agricultural and forest machines. This standard allows an implement to command specific functions of a tractor. Agricultural tractors can be equipped for silvicultural work forming small scale forest machine. It could cost-efficiently compete against common forest machines in some tasks. We have developed an ISO 11783 compliant forest crane connected to an agricultural tractor. The combination is designed to work as a test platform for an autonomous forest machine. The dynamics of the system have been studied using first and second-order models. Based on identification tests with no load on the crane, first-order model is sufficient for describing the motion of most of the cylinders. According to the identification results, small controls do not cause motion on the crane, and a non-linear model is required. Currently used hydraulics of agricultural tractors is not entirely adequate for controlling forest cranes. With more intelligent tractor hydraulics, the crane could be more controllable and energy-efficient.Peer reviewe

Single Photon Lidar in Mobile Laser Scanning: The Sampling Rate Problem and Initial Solution via Spatial Correlations

Single photon lidars (in solid state form) offer several benefits over pulsed lidars, such as independence of micro-mechanical moving parts or rotating joints, lower power consumption, faster acquisition rate, and reduced size. When mass produced, they will be cheaper and smaller and thus very attractive for mobile laser scanning applications. However, as these lidars operate by receiving single photons, they are very susceptible to background illumination such as sunlight. In other words, the observations contain a significant amount of noise, or to be specific, outliers. This causes trouble for measurements done in motion, as the sampling rate (i.e. the measurement frequency) should be low and high at the same time. It should be low enough so that target detection is robust, meaning that the targets can be distinguished from the single-photon avalanche diode (SPAD) triggings caused by the background photons. On the other hand, the sampling rate should be high enough to allow for measurements to be done from motion. Quick sampling reduces the probability that a sample gathered during motion would contain data from more than a single target at a specific range. Here, we study the exploitation of spatial correlations that exist between the observations as a mean to overcome this sampling rate paradox. We propose computational methods for short and long range. Our results indicate that the spatial correlations do indeed allow for faster and more robust sampling of measurements, which makes single photon lidars more attractive in (daylight) mobile laser scanning

A Long-Term Terrestrial Laser Scanning Measurement Station to Continuously Monitor Structural and Phenological Dynamics of Boreal Forest Canopy

The terrestrial laser scanner (TLS) has become standard technology for vegetation dynamics monitoring. TLS time series have significant underlying application in investigating structural development and dynamics on a daily and seasonal scale. However, the high potential of TLS for the monitoring of long-term temporal phenomena in fully grown trees with high spatial and temporal resolution has not yet been fully explored. Automated TLS platforms for long-term data collection and monitoring of forest dynamics are rare; and long-term TLS time series data is not yet readily available to potential end-user, such as forestry researchers and plant biologists. This work presents an automated and permanent TLS measurement station that collects high frequency and high spatial resolution TLS time series, aiming to monitor short- and long-term phenological changes at a boreal forestry field station (0.006◦ angular resolution, one scan per hour). The measurement station is the first of its kind considering the scope, accuracy, and length of the time series it produces. The TLS measurement station provides a unique dataset to monitor the 3D physical structure of a boreal forest, enabling new insights into forest dynamics. For instance, the information collected by the TLS station can be used to accurately detect structural changes in tree crowns surrounding the station. These changes and their timing can be linked with the phenological state of plants, such as the start of leaf-out during spring growing season. As the first results of this novel station, we present time series data products collected with the station and what detailed information it provides about the phenological changes in the test site during the leaf sprout in spring