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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.
Bayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors, such as Inertial Measurement Unit (IMU), have complicated error sources. Therefore, IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we developed deep Kalman filter to model and remove IMU errors and, consequently, improve the accuracy of IMU positioning. To achieve this, we added a modelling step to the prediction and update steps of the Kalman filter, so that the IMU error model is learned during integration. The results showed our deep Kalman filter outperformed the conventional Kalman filter and reached a higher level of accuracy.
Siavash HosseinyAlamdary. Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study. Sensors 2018, 18, 1316 .
AMA StyleSiavash HosseinyAlamdary. Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study. Sensors. 2018; 18 (5):1316.
Chicago/Turabian StyleSiavash HosseinyAlamdary. 2018. "Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study." Sensors 18, no. 5: 1316.
The Bayes filters, such as Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of the unknowns. Efficient integration of multiple sensors requires deep knowledge of their error sources and it is not trivial for complicated sensors, such as Inertial Measurement Unit (IMU). Therefore, IMU error modelling and efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we develop deep Kalman filter to model and remove IMU errors and consequently, improve the accuracy of IMU positioning. In other words, we add modelling step to the prediction and update steps of Kalman filter and the IMU error model is learned during integration. Therefore, our deep Kalman filter outperforms Kalman filter and reaches higher accuracy.
Siavash HosseinyAlamdary. Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; GNSS/IMU Case Study. 2018, 1 .
AMA StyleSiavash HosseinyAlamdary. Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; GNSS/IMU Case Study. . 2018; ():1.
Chicago/Turabian StyleSiavash HosseinyAlamdary. 2018. "Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; GNSS/IMU Case Study." , no. : 1.