Browsing by Author "Lehtola, Ville V."

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  • Comparison of the Selected State-Of-The-Art 3D Indoor Scanning and Point Cloud Generation Methods
    (2017-08) Lehtola, Ville V.; Kaartinen, Harri; Nüchter, Andreas; Kaijaluoto, Risto; Kukko, Antero; Litkey, Paula; Honkavaara, Eija; Rosnell, Tomi; Vaaja, Matti T.; Virtanen, Juho-Pekka; Kurkela, Matti; El Issaoui, Aimad; Zhu, Lingli; Jaakkola, Anttoni; Hyyppä, Juha
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Accurate three-dimensional (3D) data from indoor spaces are of high importance for various applications in construction, indoor navigation and real estate management. Mobile scanning techniques are offering an efficient way to produce point clouds, but with a lower accuracy than the traditional terrestrial laser scanning (TLS). In this paper, we first tackle the problem of how the quality of a point cloud should be rigorously evaluated. Previous evaluations typically operate on some point cloud subset, using a manually-given length scale, which would perhaps describe the ranging precision or the properties of the environment. Instead, the metrics that we propose perform the quality evaluation to the full point cloud and over all of the length scales, revealing the method precision along with some possible problems related to the point clouds, such as outliers, over-completeness and misregistration. The proposed methods are used to evaluate the end product point clouds of some of the latest methods. In detail, point clouds are obtained from five commercial indoor mapping systems, Matterport, NavVis, Zebedee, Stencil and Leica Pegasus: Backpack, and three research prototypes, Aalto VILMA, FGI Slammer and the Wurzburg backpack. These are compared against survey-grade TLS point clouds captured from three distinct test sites that each have different properties. Based on the presented experimental findings, we discuss the properties of the proposed metrics and the strengths and weaknesses of the above mapping systems and then suggest directions for future research.
  • Erratum: Ville V. Lehtola, et al. Radial Distortion from Epipolar Constraint for Rectilinear Cameras
    (2017-06-23) Lehtola, Ville V.; Kurkela, Matti; Rönnholm, Petri
     Other contribution
  • Forestry crane posture estimation with a two-dimensional laser scanner
    (2018-10-01) Hyyti, Heikki; Lehtola, Ville V.; Visala, Arto
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Crane posture estimation is the stepping stone to forest machine automation. Here, we introduce a robust minimal perception solution, that is, one that uses minimal constraints for maximal benefits. Specifically, we introduce a robust particle-filter-based method to estimate and track the posture of a flexible hydraulic crane by using only low-cost equipment, namely, a two-dimensional (2D) laser scanner, two short magnetically attached metal tubes as targets, and an angle sensor. An important feature of our method is that it incorporates control signals for hydraulic actuators. In contrast to the previous works employing laser scanners, we do not use the full shape of the crane to estimate the crane posture, but, instead, we use only two small targets in the field of view of the laser scanner. Thus, a large share of the range data is useful for other purposes, for example, to map the surrounding environment. We test the proposed method in a challenging forest environment and show that the particle filter is able to estimate the posture of the hydraulic crane efficiently and reliably in the presence of occlusions and obstructions. During our comprehensive testing, the tip position was measured with average errors smaller than 4.3 cm whereas the absolute maximum error was 15 cm.
  • Graph SLAM correction for single scanner MLS forest data under boreal forest canopy
    (2017-10-01) Kukko, Antero; Kaijaluoto, Risto; Kaartinen, Harri; Lehtola, Ville V.; Jaakkola, Anttoni; Hyyppä, Juha
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Mobile laser scanning (MLS) provides kinematic means to collect three dimensional data from surroundings for various mapping and environmental analysis purposes. Vehicle based MLS has been used for road and urban asset surveys for about a decade. The equipment to derive the trajectory information for the point cloud generation from the laser data is almost without exception based on GNSS-IMU (Global Navigation Satellite System – Inertial Measurement Unit) technique. That is because of the GNSS ability to maintain global accuracy, and IMU to produce the attitude information needed to orientate the laser scanning and imaging sensor data. However, there are known challenges in maintaining accurate positioning when GNSS signal is weak or even absent over long periods of time. The duration of the signal loss affects the severity of degradation of the positioning solution depending on the quality/performance level of the IMU in use. The situation could be improved to a certain extent with higher performance IMUs, but increasing system expenses make such approach unsustainable in general. Another way to tackle the problem is to attach additional sensors to the system to overcome the degrading position accuracy: such that observe features from the environment to solve for short term system movements accurately enough to prevent the IMU solution to drift. This results in more complex system integration with need for more calibration and synchronization of multiple sensors into an operational approach. In this paper we study operation of an ATV (All -terrain vehicle) mounted, GNSS-IMU based single scanner MLS system in boreal forest conditions. The data generated by RoamerR2 system is targeted for generating 3D terrain and tree maps for optimizing harvester operations and forest inventory purposes at individual tree level. We investigate a process-flow and propose a graph optimization based method which uses data from a single scanner MLS for correcting the post-processed GNSS-IMU trajectory for positional drift under mature boreal forest canopy conditions. The result shows that we can improve the internal conformity of the data significantly from 0.7 m to 1 cm based on tree stem feature location data. When the optimization result is compared to reference at plot level we reach down to 6 cm mean error in absolute tree stem locations. The approach can be generalized to any MLS point cloud data, and provides as such a remarkable contribution to harness MLS for practical forestry and high precision terrain and structural modeling in GNSS obstructed environments.
  • Mobile mapping of night-time road environment lighting conditions
    (2018-12-20) Vaaja, Matti; Kurkela, Matti; Maksimainen, Mikko; Virtanen, Juho-Pekka; Kukko, Antero; Lehtola, Ville V.; Hyyppä, Juha; Hyyppä, Hannu
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    The measurement of 3D geometry for road environments is one of the main applications of mobile mapping systems (MMS). We present mobile mapping applied to a night-time road environment. We integrate the measurement of luminances into a georeferenced 3D point cloud. The luminance measurement and the 3D point cloud acquired with an MMS are used in assessing road environment lighting conditions. Luminance (cd/m2) was measured with a luminance-calibrated panoramic camera system, and point cloud was produced by laser scanners. The relative orientation between the GNSS, IMU, camera, and laser scanner sensors was solved in order to integrate the data sets into the same coordinate system. Hence, the georeferenced luminance values are transferable into geographic information systems (GIS). The method provides promising results for future road lighting assessment. In addition, this article demonstrates the night-time mobile mapping principle applied to a road section in Helsinki, Finland. Finally, we discuss the future applications of mobile-mapped luminance point clouds.
  • Preregistration classification of mobile LIDAR data using spatial correlations
    (2019-09-01) Lehtola, Ville V.; Lehtomaki, Matti; Hyyti, Heikki; Kaijaluoto, Risto; Kukko, Antero; Kaartinen, Harri; Hyyppa, Juha
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    We explore a novel paradigm for light detection and ranging (LIDAR) point classification in mobile laser scanning (MLS). In contrast to the traditional scheme of performing classification for a 3-D point cloud after registration, our algorithm operates on the raw data stream classifying the points on-the-fly before registration. Hence, we call it preregistration classification (PRC). Specifically, this technique is based on spatial correlations, i.e., local range measurements supporting each other. The proposed method is general since exact scanner pose information is not required, nor is any radiometric calibration needed. Also, we show that the method can be applied in different environments by adjusting two control parameters, without the results being overly sensitive to this adjustment. As results, we present classification of points from an urban environment where noise, ground, buildings, and vegetation are distinguished from each other, and points from the forest where tree stems and ground are classified from the other points. As computations are efficient and done with a minimal cache, the proposed methods enable new on-chip deployable algorithmic solutions. Broader benefits from the spatial correlations and the computational efficiency of the PRC scheme are likely to be gained in several online and offline applications. These range from single robotic platform operations including simultaneous localization and mapping (SLAM) algorithms to wall-clock time savings in geoinformation industry. Finally, PRC is especially attractive for continuous-beam and solid-state LIDARs that are prone to output noisy data.
  • Radial Distortion from Epipolar Constraint for Rectilinear Cameras
    (2017-01-24) Lehtola, Ville V.; Kurkela, Matti; Rönnholm, Petri
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Lens distortion causes difficulties for 3D reconstruction, when uncalibrated image sets with weak geometry are used. We show that the largest part of lens distortion, known as the radial distortion, can be estimated along with the center of distortion from the epipolar constraint separately and before bundle adjustment without any calibration rig. The estimate converges as more image pairs are added. Descriptor matched scale-invariant feature (SIFT) point pairs that contain false matches can readily be given to our algorithm, EPOS (EpiPOlar-based Solver), as input. The processing is automated to the point where EPOS solves the distortion whether its type is barrel or pincushion or reports if there is no need for correction.
  • Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review
    (2022-01-01) Thombre, Sarang; Zhao, Zheng; Ramm-Schmidt, Henrik; Vallet Garcia, Jose M.; Malkamäki, Tuomo; Nikolskiy, Sergey; Hammarberg, Toni; Nuortie, Hiski; Bhuiyan, M. Zahidul H.; Särkkä, Simo; Lehtola, Ville V.
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Autonomous ships are expected to improve the level of safety and efficiency in future maritime navigation. Such vessels need perception for two purposes: to perform autonomous situational awareness and to monitor the integrity of the sensor system itself. In order to meet these needs, the perception system must fuse data from novel and traditional perception sensors using Artificial Intelligence (AI) techniques. This article overviews the recognized operational requirements that are imposed on regular and autonomous seafaring vessels, and then proceeds to consider suitable sensors and relevant AI techniques for an operational sensor system. The integration of four sensors families is considered: sensors for precise absolute positioning (Global Navigation Satellite System (GNSS) receivers and Inertial Measurement Unit (IMU)), visual sensors (monocular and stereo cameras), audio sensors (microphones), and sensors for remote-sensing (RADAR and LiDAR). Additionally, sources of auxiliary data, such as Automatic Identification System (AIS) and external data archives are discussed. The perception tasks are related to well-defined problems, such as situational abnormality detection, vessel classification, and localization, that are solvable using AI techniques. Machine learning methods, such as deep learning and Gaussian processes, are identified to be especially relevant for these problems. The different sensors and AI techniques are characterized keeping in view the operational requirements, and some example state-of-the-art options are compared based on accuracy, complexity, required resources, compatibility and adaptability to maritime environment, and especially towards practical realization of autonomous systems.