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Positioning is a need for many applications related to mapping and navigation either in civilian or military domains. The significant developments in satellite-based techniques, sensors, telecommunications, computer hardware and software, image processing, etc. positively influenced to solve the positioning problem efficiently and instantaneously. Accordingly, the mentioned development empowered the applications and advancement of autonomous navigation. One of the most interesting developed positioning techniques is what is called in robotics as the Simultaneous Localization and Mapping SLAM. The SLAM problem solution has witnessed a quick improvement in the last decades either using active sensors like the RAdio Detection And Ranging (Radar) and Light Detection and Ranging (LiDAR) or passive sensors like cameras. Definitely, positioning and mapping is one of the main tasks for Geomatics engineers, and therefore it's of high importance for them to understand the SLAM topic which is not easy because of the huge documentation and algorithms available and the various SLAM solutions in terms of the mathematical models, complexity, the sensors used, and the type of applications. In this paper, a clear and simplified explanation is introduced about SLAM from a Geomatical viewpoint avoiding going into the complicated algorithmic details behind the presented techniques. In this way, a general overview of SLAM is presented showing the relationship between its different components and stages like the core part of the front-end and back-end and their relation to the SLAM paradigm. Furthermore, we explain the major mathematical techniques of filtering and pose graph optimization either using visual or LiDAR SLAM and introduce a summary of the deep learning efficient contribution to the SLAM problem. Finally, we address examples of some existing practical applications of SLAM in our reality.
Bashar Alsadik; Samer Karam. The Simultaneous Localization and Mapping (SLAM)-An Overview. Surveying and Geospatial Engineering Journal 2021, 2, 01 -12.
AMA StyleBashar Alsadik, Samer Karam. The Simultaneous Localization and Mapping (SLAM)-An Overview. Surveying and Geospatial Engineering Journal. 2021; 2 (01):01-12.
Chicago/Turabian StyleBashar Alsadik; Samer Karam. 2021. "The Simultaneous Localization and Mapping (SLAM)-An Overview." Surveying and Geospatial Engineering Journal 2, no. 01: 01-12.
In recent years, the importance of indoor mapping increased in a wide range of applications, such as facility management and mapping hazardous sites. The essential technique behind indoor mapping is simultaneous localization and mapping (SLAM) because SLAM offers suitable positioning estimates in environments where satellite positioning is not available. State-of-the-art indoor mobile mapping systems employ Visual-based SLAM or LiDAR-based SLAM. However, Visual-based SLAM is sensitive to textureless environments and, similarly, LiDAR-based SLAM is sensitive to a number of pose configurations where the geometry of laser observations is not strong enough to reliably estimate the six-degree-of-freedom (6DOF) pose of the system. In this paper, we present different strategies that utilize the benefits of the inertial measurement unit (IMU) in the pose estimation and support LiDAR-based SLAM in overcoming these problems. The proposed strategies have been implemented and tested using different datasets and our experimental results demonstrate that the proposed methods do indeed overcome these problems. We conclude that IMU observations increase the robustness of SLAM, which is expected, but also that the best reconstruction accuracy is obtained not with a blind use of all observations but by filtering the measurements with a proposed reliability measure. To this end, our results show promising improvements in reconstruction accuracy.
S. Karam; Ville Lehtola; G. Vosselman. STRATEGIES TO INTEGRATE IMU AND LIDAR SLAM FOR INDOOR MAPPING. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2020, V-1-2020, 223 -230.
AMA StyleS. Karam, Ville Lehtola, G. Vosselman. STRATEGIES TO INTEGRATE IMU AND LIDAR SLAM FOR INDOOR MAPPING. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2020; V-1-2020 ():223-230.
Chicago/Turabian StyleS. Karam; Ville Lehtola; G. Vosselman. 2020. "STRATEGIES TO INTEGRATE IMU AND LIDAR SLAM FOR INDOOR MAPPING." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-1-2020, no. : 223-230.
Indoor mapping techniques are highly important in many applications, such as human navigation and indoor modelling. As satellite positioning systems do not work in indoor applications, several alternative navigational sensors and methods have been used to provide accurate indoor positioning for mapping purposes, such as inertial measurement units (IMUs) and simultaneous localisation and mapping algorithms (SLAM). In this paper, we investigate the benefits that the integration of a low-cost microelectromechanical system (MEMS) IMU can bring to a feature-based SLAM algorithm. Specifically, we utilize IMU data to predict the pose of our backpack indoor mobile mapping system to improve the SLAM algorithm. The experimental results show that using the proposed IMU integration method leads into a more robust data association between the measured points and the model planes. Notably, the number of points that are assigned to the model planes is increased, and the root mean square error (RMSE) of the residuals, i.e. distances between these measured points and the model planes, is decreased significantly from 1.8 cm to 1.3 cm.
S. Karam; Ville Lehtola; G. Vosselman. INTEGRATING A LOW-COST MEMS IMU INTO A LASER-BASED SLAM FOR INDOOR MOBILE MAPPING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-2/W17, 149 -156.
AMA StyleS. Karam, Ville Lehtola, G. Vosselman. INTEGRATING A LOW-COST MEMS IMU INTO A LASER-BASED SLAM FOR INDOOR MOBILE MAPPING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-2/W17 ():149-156.
Chicago/Turabian StyleS. Karam; Ville Lehtola; G. Vosselman. 2019. "INTEGRATING A LOW-COST MEMS IMU INTO A LASER-BASED SLAM FOR INDOOR MOBILE MAPPING." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W17, no. : 149-156.
Indoor mobile mapping systems are important for a wide range of applications starting from disaster management to straightforward indoor navigation. This paper presents the design and performance of a low-cost backpack indoor mobile mapping system (ITC-IMMS) that utilizes a combination of laser range-finders (LRFs) to fully recover the 3D building model based on a feature-based simultaneous localization and mapping (SLAM) algorithm. Specifically, we use robust planar features. These are advantageous, because oftentimes the final representation of the indoor environment is wanted in a planar form, and oftentimes the walls in an indoor environment physically have planar shapes. In order to understand the potential accuracy of our indoor models and to assess the system’s ability to capture the geometry of indoor environments, we develop novel evaluation techniques. In contrast to the state-of-the-art evaluation methods that rely on ground truth data, our evaluation methods can check the internal consistency of the reconstructed map in the absence of any ground truth data. Additionally, the external consistency can be verified with the often available as-planned state map of the building. The results demonstrate that our backpack system can capture the geometry of the test areas with angle errors typically below 1.5° and errors in wall thickness around 1 cm. An optimal configuration for the sensors is determined through a set of experiments that makes use of the developed evaluation techniques.
Samer Karam; George Vosselman; Michael Peter; Siavash Hosseinyalamdary; Ville Lehtola. Design, Calibration, and Evaluation of a Backpack Indoor Mobile Mapping System. Remote Sensing 2019, 11, 905 .
AMA StyleSamer Karam, George Vosselman, Michael Peter, Siavash Hosseinyalamdary, Ville Lehtola. Design, Calibration, and Evaluation of a Backpack Indoor Mobile Mapping System. Remote Sensing. 2019; 11 (8):905.
Chicago/Turabian StyleSamer Karam; George Vosselman; Michael Peter; Siavash Hosseinyalamdary; Ville Lehtola. 2019. "Design, Calibration, and Evaluation of a Backpack Indoor Mobile Mapping System." Remote Sensing 11, no. 8: 905.
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.
S. Karam; M. Peter; S. Hosseinyalamdary; G. Vosselman. AN EVALUATION PIPELINE FOR INDOOR LASER SCANNING POINT CLOUDS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2018, IV-1, 85 -92.
AMA StyleS. Karam, M. Peter, S. Hosseinyalamdary, G. Vosselman. AN EVALUATION PIPELINE FOR INDOOR LASER SCANNING POINT CLOUDS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2018; IV-1 ():85-92.
Chicago/Turabian StyleS. Karam; M. Peter; S. Hosseinyalamdary; G. Vosselman. 2018. "AN EVALUATION PIPELINE FOR INDOOR LASER SCANNING POINT CLOUDS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-1, no. : 85-92.