Progress on isprs benchmark on multisensory indoor mapping and positioning

This paper presents the design of the benchmark dataset on multisensory indoor mapping and position (MIMAP) which is sponsored by ISPRS scientific initiatives. The benchmark dataset including point clouds captured by indoor mobile laser scanning system (IMLS) in indoor environments of various complexity. The benchmark aims to stimulate and promote research in the following three fields: (1) SLAM-based indoor point cloud generation; (2) automated BIM feature extraction from point clouds, with an emphasis on theelements, such as floors, walls, ceilings, doors, windows, stairs, lamps, switches, air outlets, that are involved in building managementand navigation tasks ; and (3) low-cost multisensory indoor positioning, focusing on the smartphone platform solution. MIMAP provides a common framework for the evaluation and comparison of LiDAR-based SLAM, BIM feature extraction, and smartphoneindoor positioning methods

LINE SEGMENTATION OF 2D LASER SCANNER POINT CLOUDS FOR INDOOR SLAM BASED ON A RANGE OF RESIDUALS

Indoor mobile laser scanning (IMLS) based on the Simultaneous Localization and Mapping (SLAM) principle proves to be the preferred method to acquire data of indoor environments at a large scale. In previous work, we proposed a backpack IMLS system containing three 2D laser scanners and an according SLAM approach. The feature-based SLAM approach solves all six degrees of freedom simultaneously and builds on the association of lines to planes. Because of the iterative character of the SLAM process, the quality and reliability of the segmentation of linear segments in the scanlines plays a crucial role in the quality of the derived poses and consequently the point clouds. The orientations of the lines resulting from the segmentation can be influenced negatively by narrow objects which are nearly coplanar with walls (like e.g. doors) which will cause the line to be tilted if those objects are not detected as separate segments. State-of-the-art methods from the robotics domain like Iterative End Point Fit and Line Tracking were found to not handle such situations well. Thus, we describe a novel segmentation method based on the comparison of a range of residuals to a range of thresholds. For the definition of the thresholds we employ the fact that the expected value for the average of residuals of n points with respect to the line is σ / √n. Our method, as shown by the experiments and the comparison to other methods, is able to deliver more accurate results than the two approaches it was tested against

ISPRS BENCHMARK ON MULTISENSORY INDOOR MAPPING AND POSITIONING

Abstract. In this paper, we present a publicly available benchmark dataset on multisensorial indoor mapping and positioning (MiMAP), which is sponsored by ISPRS scientific initiatives. The benchmark dataset includes point clouds captured by an indoor mobile laser scanning system in indoor environments of various complexity. The benchmark aims to stimulate and promote research in the following three fields: (1) LiDAR-based Simultaneous Localization and Mapping (SLAM); (2) automated Building Information Model (BIM) feature extraction; and (3) multisensory indoor positioning. The MiMAP project provides a common framework for the evaluation and comparison of LiDAR-based SLAM, BIM feature extraction, and smartphone-based indoor positioning methods. This paper describes the multisensory setup, data acquisition process, data description, challenges, and evaluation metrics included in the MiMAP project

MOBILE LASER SCANNING SYSTEMS FOR GPS/GNSS-DENIED ENVIRONMENT MAPPING

Indoor 3D mapping provides a useful three-dimensional structure via an indoor map for many applications. To acquire highly efficient and relatively accurate mapping for large-scale GPS/GNSS-denied scene, we present an upgraded backpacked laser scanning system and a car-mounted indoor mobile laser scanning system. The systems provide both 3D laser scanning point cloud and camera images. In this paper, a simultaneous extrinsic calibration approach for multiple multi-beam LIDAR and multiple cameras is also proposed using the Simultaneous Localization and Mapping (SLAM)-based algorithm. The proposed approach uses the SLAM-based algorithm to achieve a large calibration scene using mobile platforms, registers an acquired multi-beam LIDAR point cloud to the terrestrial LIDAR point cloud to acquire denser points for corner feature extraction, and finally achieves simultaneous calibration. With the proposed mapping and calibration algorithms, we can provide centimetre-lever coloured point cloud with relatively high efficiency and accuracy

AN EVALUATION PIPELINE FOR INDOOR LASER SCANNING POINT CLOUDS

The necessity for the modelling of building interiors has encouraged researchers in recent years to focus on improving the capturing and modelling techniques for such environments. State-of-the-art indoor mobile mapping systems use a combination of laser scanners and/or cameras mounted on movable platforms and allow for capturing 3D data of buildings’ interiors. As GNSS positioning does not work inside buildings, the extensively investigated Simultaneous Localisation and Mapping (SLAM) algorithms seem to offer a suitable solution for the problem. Because of the dead-reckoning nature of SLAM approaches, their results usually suffer from registration errors. Therefore, indoor data acquisition has remained a challenge and the accuracy of the captured data has to be analysed and investigated. In this paper, we propose to use architectural constraints to partly evaluate the quality of the acquired point cloud in the absence of any ground truth model. The internal consistency of walls is utilized to check the accuracy and correctness of indoor models. In addition, we use a floor plan (if available) as an external information source to check the quality of the generated indoor model. The proposed evaluation method provides an overall impression of the reconstruction accuracy. Our results show that perpendicularity, parallelism, and thickness of walls are important cues in buildings and can be used for an internal consistency check

Three‐dimensional reconstruction of fluvial surface sedimentology and topography using personal mobile laser scanning

High resolution quantification of fluvial topography has been enabled by a number of geomatics technologies. Hyperscale surveys with spatial extents of <1 km2 have been widely demonstrated by means of Terrestrial Laser Scanning (TLS) and Structure from Motion (SfM) photogrammetry. Recent advances in the development and integration of Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU) and lightweight laser scanning technologies are now resulting in the emergence of personal mobile laser scanners (MLS) that have the potential to increase data acquisition and processing rates by 1‐2 orders of magnitude compared to TLS/SfM, and thus challenge the recent dominance of these technologies. This investigation compares a personal MLS survey using a Leica Pegasus Backpack that integrates Velodyne Puck VLP‐16 sensors, and a multi‐station static TLS survey using a Riegl VZ‐1000 scanner, to produce Digital Elevation Models (DEMs) and surface sedimentology maps. The assessment is undertaken on a 500 m long reach of the braided River Feshie. Comparison to 107 independent Real Time Kinematic (RTK)‐GNSS check points resulted in similar Mean Error (ME) and Standard Deviation Error (SDE) for TLS (ME = ‐0.025 m; SDE = 0.038 m) and personal MLS (ME = ‐0.014 m; SDE = 0.019 m). Direct cloud‐to‐cloud (C2C) comparison between a sample of TLS and personal MLS observations (2.8 million points) revealed that C2C distances follows a sharply decreasing Burr distribution (a=2.35 b=3.19, rate parameter s = 9.53). Empirical relationships between sub‐metre topographic variability and median sediment grain size (10‐100 mm) demonstrate that surface roughness from personal MLS can be used to map median grain size. Differences between TLS and personal MLS empirical relationships suggest such relationships are dependent on survey technique. Personal MLS offers distinct logistical advantages over SfM photogrammetry and TLS for particular survey situations and is likely to become a widely applied technique

“AccessBIM” - A Model of Environmental Characteristics for Vision Impaired Indoor Navigation and Way Finding

The complexity of modern indoor environments has made navigation difficult for individuals with vision impairment. Hence, this thesis presents the AccessBIM framework, which is an optimized database that’s facilitates generation of a real-time floor plan with path determination. The AccessBIM framework has the potential to play an integral role in improving the independence and quality of life for people with vision impairment whilst also decreasing the cost to the community related to caretakers

Arquitectura para robots de búsqueda y rescate urbano mediante el uso de algoritmos de anti-feromonas

[ES] El atentado del 11 de septiembre de 2001 fue el ataque terrorista con mayor mortalidad en la historia de la humanidad, con un resultado de 2.996 muertes y mas de 25.000 heridos. Entre las víctimas, un total de 343 bomberos y 72 policías perdieron sus vidas. La muerte de una gran parte de estas personas, y en especial de los servicios de emergencia, fue a causa del peligro que ataña acceder a través de los escombros de los edificios derruidos. Dada la situación, varios equipo y universidades que disponían de robots de rescate, acudieron hasta la zona cero para ayudar en la ardua tarea de buscar víctimas con vida. Este fatídico evento provocó el auge de la investigación en el ámbito de la Búsqueda y Rescate Urbano. Desde entonces hasta el día de hoy, se han empleado robots como respuesta a una catástrofe en diversas ocasiones. En este trabajo se ha desarrollado una arquitectura para el uso de un enjambre de robots heterogéneo y semi-supervisado en un entorno de Búsqueda y Rescate Urbano. Más concretamente, la arquitectura permite la combinación de diversos algoritmos orientados a este ámbito para la obtención de un sistema complejo y a su vez independiente tanto del hardware como de los métodos usados. Además, se propone una nueva estrategia de exploración colaborativa basada en el comportamiento social de las hormigas. El algoritmo planteado hace uso de feromonas repelentes como mecanismo para fomentar la exploración en entornos desconocidos. Para el análisis y prueba del algoritmo y la arquitectura propuestos en este trabajo, se han diseñado una serie de experimentos. En primer lugar se ha analizado el comportamiento del algoritmo de exploración con anti-feromonas en entornos acotados basados en topologías de rejilla y de laberinto; posteriormente se han realizado en un entorno real. Los experimentos han sido estudiados tanto con simulaciones como con robots reales. Para el análisis de la arquitectura planteada, se ha implementado un sistema de búsqueda y rescate completo sobre un robot Jetbot de Nvidia, el cual ha sido probado en un entorno real. Para finalizar, se demuestra cómo la arquitectura planteada y el algoritmo propuestos son soluciones adecuadas para su uso en respuesta a una catástrofe. Además, la arquitectura planteada en este trabajo también puede permitir el uso de algoritmos que surjan en el futuro