iNav Indoor Positioning and Navigation System

Getting a pin-point accurate location is a difficult task and prone to many errors, thus providing a wrong location. Existing GPS Based Location service has proved to be moderately reliable, but it is not the same when considering indoor wise location. Getting an indoor location inside a building is harder than ever since due the signal barriers and narrow range. In this article a system for Indoor Positioning and Navigating with customized maps is presented. This will make the users to get to know the location very quickly and easily. It will be more applicable for the Government Departments where it is difficult to find a proper location. The RSSI of the wireless access points will be used to triangulate the location

An automated 3D modeling of topological indoor navigation network

© 2015, Springer Science+Business Media Dordrecht. Indoor navigation is important for various applications such as disaster management, building modeling, safety analysis etc. In the last decade, indoor environment has been a focus of wide research that includes development of indoor data acquisition techniques, 3D data modeling and indoor navigation. In this research, an automated method for 3D modeling of indoor navigation network has been presented. 3D indoor navigation modeling requires a valid 3D model that can be represented as a cell complex: a model without any gap or intersection such that two cells (e.g. room, corridor) perfectly touch each other. This research investigates an automated method for 3D modeling of indoor navigation network using a geometrical model of indoor building environment. In order to reduce time and cost of surveying process, Trimble LaserAce 1000 laser rangefinder was used to acquire indoor building data which led to the acquisition of an inaccurate geometry of building. The connection between surveying benchmarks was established using Delaunay triangulation. Dijkstra algorithm was used to find shortest path in between building floors. The modeling results were evaluated against an accurate geometry of indoor building environment which was acquired using highly-accurate Trimble M3 total station. This research intends to investigate and propose a novel method of topological navigation network modeling with a less accurate geometrical model to overcome the need of required an accurate geometrical model. To control the uncertainty of the calibration and of the reconstruction of the building from the measurements, interval analysis and homotopy continuation will be investigated in the near future

An autonomous ultra-wide band-based attitude and position determination technique for indoor mobile laser scanning

Mobile laser scanning (MLS) has been widely used in three-dimensional (3D) city modelling data collection, such as Google cars for Google Map/Earth. Building Information Modelling (BIM) has recently emerged and become prominent. 3D models of buildings are essential for BIM. Static laser scanning is usually used to generate 3D models for BIM, but this method is inefficient if a building is very large, or it has many turns and narrow corridors. This paper proposes using MLS for BIM 3D data collection. The positions and attitudes of the mobile laser scanner are important for the correct georeferencing of the 3D models. This paper proposes using three high-precision ultra-wide band (UWB) tags to determine the positions and attitudes of the mobile laser scanner. The accuracy of UWB-based MLS 3D models is assessed by comparing the coordinates of target points, as measured by static laser scanning and a total station survey

On the right track : comfort and confusion in indoor environments

Indoor navigation systems are not well adapted to the needs of their users. The route planning algorithms implemented in these systems are usually limited to shortest path calculations or derivatives, minimalizing Euclidian distance. Guiding people along routes that adhere better to their cognitive processes could ease wayfinding in indoor environments. This paper examines comfort and confusion perception during wayfinding by applying a mixed-method approach. The aforementioned method combined an exploratory focus group and a video-based online survey. From the discussions in the focus group, it could be concluded that indoor wayfinding must be considered at different levels: the local level and the global level. In the online survey, the focus was limited to the local level, i.e., local environmental characteristics. In this online study, the comfort and confusion ratings of multiple indoor navigation situations were analyzed. In general, the results indicate that open spaces and stairs need to be taken into account in the development of a more cognitively-sounding route planning algorithm. Implementing the results in a route planning algorithm could be a valuable improvement of indoor navigation support

SEAMLESS REALTIME LANE LEVEL VEHICULAR NAVIGATION IN GNSS CHALLENGING ENVIRONMENTS USING A RTK GNSS/IMU/VINS INTEGRATION SCHEME

Outdoor positioning requires a reliable solution that can work in environments where satellite signals are often blocked or degraded. Global Navigation Satellite System (GNSS) is a common choice, but it may not provide accurate results for land vehicles. To address this challenge, this research proposes a multi-sensor integrated system for vehicle navigation that combines GNSS with other sensors. The system uses Extended Kalman Filter (EKF) to fuse the data from different sources and improve the navigation performance. The algorithm targets to provide seamless navigation for urban environments as well as various indoor environments fields with INS/GNSS/VIO aiding integrated solutions. The experimental vehicle of this research is equipped with a tactical-grade inertial sensing measurement unit (IMU) as the test system, a self-designed and assembled visual platform, which includes a camera with a time synchronization protocol and a low-cost IMU. Also, both indoor experimental fields and outdoor urban scenarios with different high challenging were tested to verify the developed algorithm. To evaluate the performance of the proposed real-time navigation system, we use a high-accuracy navigation-grade system as a reference, which provides a stable and reliable trajectory. The result indicates that using the GNSS RTK solution with VIO aiding integration scheme reduced the RMS errors in long outage (450 sec, 1812 m) by 87.4% and 79.9% in position and velocity error, respectively. In urban scenario, the along-track/cross-track maximum errors can achieve 1.4 m / 1.5 m. Overall, these contribute to the development of real-time navigation systems for self-driving vehicle in the future

Rancang Bangun Sistem Penjejakan Garis Berbasis Visi Komputer pada Indoor Patrolling Drone

Indoor Patrolling drone adalah drone yang beroprasi di dalam ruangan dan dapat berpatroli pada area yang ditentukan. Patrolling drone mampu melakukan navigasi di dalam ruangan tanpa bergantung pada GPS. Kemampuan patrolling drone untuk tidak bergantung pada GPS sangat diperlukan karena ketika drone berada di dalam ruangan, sistem navigasi menggunakan GPS tidak dapat dilakukan secara optimal. Indoor patrolling drone memadukan quadcopter drone dengan sistem navigasi penjejakan garis. Penjejakan garis dilakukan untuk menentukan rute pengawasan yang akan dilalui oleh drone. Patrolling drone diperlukan karena hingga saat ini sebagian besar metode pengawasan dalam ruangan masih dilakukan oleh agen manusia yang melakukan patroli pada waktu dan area yang ditentukan. Metode tersebut masih rentan akan adanya kesalahan yang ditimbulakan oleh manusia atau sering disebut dengan human error. Patrolling drone diharapkan dapat menjadi alternatif dan meningkatkan sistem kemanan tersebut. Patrolling drone yang dimaksud pada tugas akhir ini telah berhasil dibuat dan telah dilakukan berbagai pengujian. Berdasarkan hasil pengujian tersebut, patrolling drone memiliki rata – rata durasi terbang selama 6 menit 56 detik. Sistem penjejakan garis yang digunakan pada drone ini memiliki akurasi estimasi jarak sebesar 0.51cm dan akurasi kecepatan sebesar 15.66cm/s. Dengan membandingkan dengan performa sistem navigasi dalam ruangan menggunakan GPS yang memiliki akurasi sebesar 4.5 hingga 8 meter, sistem navigasi penjejakan garis pada drone ini menunjukkan peningkatan tingkat akurasi yang cukup signifikan. Selain itu, sistem navigasi penjejakan garis pada patrolling drone ini dapat dilakukan dengan optimal hingga kecepatan 48cm/s. =============================================================================================================================== Indoor patrolling drone is a drone which operate indoor and can automatically patrol in the designated area. Patrolling drone is capable of indoor navigation without relying on GPS system. The ability of indoor patrolling drone to navigate without relying on GPS system is very important, because any form of GPS basednavigation system can not be used effectively in indoor applications. Indoor patrolling drone combines quadcopter drone and line follower navigation system. Line follower system is used to determine drone’s surveillance route. Self-patrolling drone is needed because untill now, most of the surveillance method are still done by human. Human surveillance method are prone to error caused by human or frequently labeled as human error. Self-patrolling drone is expected to be an alternative and to improve conventional surveillance method that involve human in their system. Self-patrolling drone described in this final project have been successfully made and has undergone several test and observation. Based on the test and observation result, self-patrolling drone has 6 minutes and 56 seconds flight time. Line follower system used in this project has distance estimation with 0.51cm accuracy and cruise control with 15.66cm/s accuracy, in contrast with GPS based indoor navigation system that has 4.5-8m accuracy, line follower navigation system shows quite significant improvement in terms of accuracy. Moreover, line follower navigation in this project can be implemented effectively up to 48cm/s cruising speed