scholarly journals Measuring the Uncertainty of Probabilistic Maps Representing Human Motion for Indoor Navigation

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Susanna Kaiser ◽  
Maria Garcia Puyol ◽  
Patrick Robertson
Keyword(s):  
Human Motion ◽  
Indoor Navigation ◽  
Global Navigation Satellite Systems ◽  
Indoor Environments ◽  
Satellite Systems ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
History Of ◽  
Important Field ◽  
Global Navigation Satellite

Indoor navigation and mapping have recently become an important field of interest for researchers because global navigation satellite systems (GNSS) are very often unavailable inside buildings. FootSLAM, a SLAM (Simultaneous Localization and Mapping) algorithm for pedestrians based on step measurements, addresses the indoor mapping and positioning problem and can provide accurate positioning in many structured indoor environments. In this paper, we investigate how to compare FootSLAM maps via two entropy metrics. Since collaborative FootSLAM requires the alignment and combination of several individual FootSLAM maps, we also investigate measures that help to align maps that partially overlap. We distinguish between the map entropy conditioned on the sequence of pedestrian’s poses, which is a measure of the uncertainty of the estimated map, and the entropy rate of the pedestrian’s steps conditioned on the history of poses and conditioned on the estimated map. Because FootSLAM maps are built on a hexagon grid, the entropy and relative entropy metrics are derived for the special case of hexagonal transition maps. The entropy gives us a new insight on the performance of FootSLAM’s map estimation process.

scholarly journals Comparison of Different Hardware Configurations for 2D SLAM Techniques Based on Google Cartographer Use Case

2021 ◽  
Author(s):  
Łukasz Sobczak ◽  
Katarzyna Filus ◽  
Joanna Domańska ◽  
Adam Domański
Keyword(s):  
Real Life ◽  
Ground Truth ◽  
Cost Effective ◽  
Global Navigation Satellite Systems ◽  
Use Case ◽  
Indoor Environments ◽  
Satellite Systems ◽  
Localization And Mapping ◽  
Hardware Configuration ◽  
Global Navigation Satellite

Abstract One of the most challenging topics in Robotics is Simultaneous Localization and Mapping (SLAM) in the indoor environments. Due to the fact that Global Navigation Satellite Systems cannot be successfully used in such environments, different data sources are used for this purpose, among others LiDARs (Light Detection and Ranging), which have advanced from numerous other technologies. Other embedded sensors can be used along with LiDARs to improve SLAM accuracy, e.g. the ones available in the Inertial Measurement Units and wheel odometry sensors. Evaluation of different SLAM algorithms and possible hardware configurations in real environments is time consuming and expensive. For that reason, in this paper we evaluate the performance of different hardware configuration used with Google Cartographer SLAM algorithms in simulation framework proposed in 1. Our use case is an actual robot used for room decontamination. The results show that for our robot the best hardware configuration consists of three LiDARs 2D, IMU and wheel odometry sensors. The proposed simulation-based methodology is a cost-effective alternative to real-world evaluation. It allows easy automation and provides access to precise ground truth. It is especially beneficial in the early stages of product design and to reduce the number of necessary real-life tests and hardware configurations.

Multi-Sensor Integrated Navigation in Urban and Indoor Environments

2018 ◽  
pp. 397-442 ◽  
Author(s):  
Mohamed Atia
Keyword(s):  
Sensor Fusion ◽  
Global Navigation Satellite Systems ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Integrated Navigation ◽  
Satellite Systems ◽  
Tightly Coupled ◽  
Sensor Processing ◽  
Global Navigation Satellite ◽  
Dense Urban Environment

The art of multi-sensor processing, or “sensor-fusion,” is the ability to optimally infer state information from multiple noisy streams of data. One major application area where sensor fusion is commonly used is navigation technology. While global navigation satellite systems (GNSS) can provide centimeter-level location accuracy worldwide, they suffer from signal availability problems in dense urban environment and they hardly work indoors. While several alternative backups have been proposed, so far, no single sensor or technology can provide the desirable precise localization in such environments under reasonable costs and affordable infrastructures. Therefore, to navigate through these complex areas, combining sensors is beneficial. Common sensors used to augment/replace GNSS in complex environments include inertial measurement unit (IMU), range sensors, and vision sensors. This chapter discusses the design and implementation of tightly coupled sensor fusion of GNSS, IMU, and light detection and ranging (LiDAR) measurements to navigate in complex urban and indoor environments.

scholarly journals Seamless Indoor-Outdoor Navigation based on GNSS, INS and Terrestrial Ranging Techniques

2017 ◽  
Vol 70 (6) ◽  
pp. 1183-1204 ◽  
Author(s):  
Wei Jiang ◽  
Yong Li ◽  
Chris Rizos ◽  
Baigen Cai ◽  
Wei Shangguan
Keyword(s):  
Navigation System ◽  
Global Navigation Satellite Systems ◽  
Indoor Environments ◽  
Integrated Navigation ◽  
Satellite Systems ◽  
Integrated Navigation System ◽  
Outdoor Environments ◽  
Federated Kalman Filter ◽  
Indoor And Outdoor Environments ◽  
Global Navigation Satellite

We describe an integrated navigation system based on Global Navigation Satellite Systems (GNSS), an Inertial Navigation System (INS) and terrestrial ranging technologies that can support accurate and seamless indoor-outdoor positioning. To overcome severe multipath disturbance in indoor environments, Locata technology is used in this navigation system. Such a “Locata-augmented” navigation system can operate in different positioning modes in both indoor and outdoor environments. In environments where GNSS is unavailable, e.g. indoors, the proposed system is designed to operate in the Locata/INS “loosely-integrated” mode. On the other hand, in outdoor environments, all GNSS, Locata and INS measurements are available, and all useful information can be fused via a decentralised Federated Kalman filter. To evaluate the proposed system for seamless indoor-outdoor positioning, an indoor-outdoor test was conducted at a metal-clad warehouse. The test results confirmed that the proposed navigation system can provide continuous and reliable position and attitude solutions, with the positioning accuracy being better than five centimetres.

scholarly journals LOCALIZATION CORRECTIONS FOR MOBILE LASER SCANNER USING LOCAL SUPPORT-BASED OUTLIER FILTERING

2016 ◽  
Vol III-4 ◽  
pp. 81-88 ◽  
Author(s):  
V. V. Lehtola ◽  
J.-P. Virtanen ◽  
P. Rönnholm ◽  
A. Nüchter
Keyword(s):  
Degrees Of Freedom ◽  
Laser Scanner ◽  
Three Dimensions ◽  
Global Navigation Satellite Systems ◽  
Dimensional Solution ◽  
Satellite Systems ◽  
Localization And Mapping ◽  
Local Support ◽  
Novel Concept ◽  
Global Navigation Satellite

Following the pioneering work introduced in [Lehtola et al., ISPRS J. Photogramm. Remote Sens. 99, 2015, pp. 25–29], we extend the state-of-the-art intrinsic localization solution for a single two-dimensional (2D) laser scanner from one into (quasi) three dimensions (3D). By intrinsic localization, we mean that no external sensors are used to localize the scanner, such as inertial measurement devices (IMU) or global navigation satellite systems (GNSS). Specifically, the proposed method builds on a novel concept of local support-based filtering of outliers, which enables the use of six degrees-of-freedom (DoF) simultaneous localization and mapping (SLAM) for the purpose of enacting appropriate trajectory corrections into the previous one-dimensional solution. Moreover, the local support-based filtering concept is platform independent, and is therefore likely to be widely generalizable. The here presented overall method is yet limited into quasi-3D by its inability to recover trajectories with steep curvature, but in the future, it may be further extended into full 3D.

scholarly journals THE HANDHELD MOBILE LASER SCANNERS AS A TOOL FOR ACCURATE POSITIONING UNDER FOREST CANOPY

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 211-218 ◽  
Author(s):  
J. Chudá ◽  
M. Hunčaga ◽  
J. Tuček ◽  
M. Mokroš
Keyword(s):  
Forest Canopy ◽  
Sustainable Forest Management ◽  
Environmental Data ◽  
Global Navigation Satellite Systems ◽  
Forest Environment ◽  
Positional Accuracy ◽  
Satellite Systems ◽  
Localization And Mapping ◽  
Laser Scanners ◽  
Global Navigation Satellite

Abstract. Nowadays it is important to shift positional accuracy of object measurements under the forest canopy closer to the accuracy standards for land surveys due to the requirements in the field of ecosystem protection, sustainable forest management, property relations, and land register. Simultaneously, it is desirable to use the technology of environmental data acquisition which is not time consuming and cost demanding. Global Navigation Satellite Systems (GNSS) are the most used for positioning today. However, the usefulness and also the accuracy of the measurements with this technology depend on various factors (the strength of the GNSS signal, the geometric position of satellites, the multipath effect etc.). Based on the above mentioned facts, the usability of technology independent of GNSS indicates an ideal solution for positioning under the forest canopy. Several studies have studied the usability of Handheld Mobile Laser Scanners (HMLS) in complex environment. The goal of this paper was to verify a new data collection approach (HMLS with Simultaneous Localization and Mapping (SLAM) technology) for the forest environment practice. The main objective of our study was to reach a precision which complies with the accuracy standards for land surveys. The RMSE of derived positions from point cloud, produced by SLAM devices were 25.3 cm and 28.4 cm, for ZEB REVO and ZEB HORIZON, the handheld mobile laser SLAM scanners used in this study. ZEB HORIZON achieved twice as big accuracy of diameter of breast height (DBH) estimation as ZEB REVO.

A Brief History of Global Navigation Satellite Systems

2011 ◽  
Vol 65 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Norman Bonnor
Keyword(s):  
Global Navigation Satellite Systems ◽  
Navigation Systems ◽  
Postgraduate Students ◽  
Satellite Systems ◽  
Space Race ◽  
History Of ◽  
1960S And 1970S ◽  
The 1960S ◽  
Satellite Navigation Systems ◽  
Global Navigation Satellite

This paper charts the history of Global Satellite Navigation Systems (GNSS) from the earliest days of the ‘Space Race’ in the 1960s and 1970s to the latest plans for modernisation of existing systems and the development of new systems yet to be deployed or become operational. The paper is based on lectures and presentations given by the author to postgraduate students at The University of Nottingham, students on the RAF General Duties Aero-Systems Course at the Air Warfare Centre, RAF Cranwell and to a number of RIN, RAeS and IEE Branches and to local aviation groups.

scholarly journals LOCALIZATION CORRECTIONS FOR MOBILE LASER SCANNER USING LOCAL SUPPORT-BASED OUTLIER FILTERING

2016 ◽  
Vol III-4 ◽  
pp. 81-88 ◽  
Author(s):  
V. V. Lehtola ◽  
J.-P. Virtanen ◽  
P. Rönnholm ◽  
A. Nüchter
Keyword(s):  
Degrees Of Freedom ◽  
Laser Scanner ◽  
Three Dimensions ◽  
Global Navigation Satellite Systems ◽  
Dimensional Solution ◽  
Satellite Systems ◽  
Localization And Mapping ◽  
Local Support ◽  
Novel Concept ◽  
Global Navigation Satellite

Following the pioneering work introduced in [Lehtola et al., ISPRS J. Photogramm. Remote Sens. 99, 2015, pp. 25–29], we extend the state-of-the-art intrinsic localization solution for a single two-dimensional (2D) laser scanner from one into (quasi) three dimensions (3D). By intrinsic localization, we mean that no external sensors are used to localize the scanner, such as inertial measurement devices (IMU) or global navigation satellite systems (GNSS). Specifically, the proposed method builds on a novel concept of local support-based filtering of outliers, which enables the use of six degrees-of-freedom (DoF) simultaneous localization and mapping (SLAM) for the purpose of enacting appropriate trajectory corrections into the previous one-dimensional solution. Moreover, the local support-based filtering concept is platform independent, and is therefore likely to be widely generalizable. The here presented overall method is yet limited into quasi-3D by its inability to recover trajectories with steep curvature, but in the future, it may be further extended into full 3D.

scholarly journals Low-Cost Indoor Positioning Application Based on Map Assistance and Mobile Phone Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4285 ◽  
Author(s):  
Yi-Shan Li ◽  
Fang-Shii Ning
Keyword(s):  
Mobile Phone ◽  
Indoor Positioning ◽  
Global Navigation Satellite Systems ◽  
Navigation Satellite ◽  
Indoor Environments ◽  
Linear Calibration ◽  
Positioning Accuracy ◽  
Satellite Systems ◽  
Global Navigation ◽  
Global Navigation Satellite

Current mainstream navigation and positioning equipment, intended for providing accurate positioning signals, comprise global navigation satellite systems, maps, and geospatial databases. Although global navigation satellite systems have matured and are widespread, they cannot provide effective navigation and positioning services in covered areas or areas lacking strong signals, such as indoor environments. To solve the problem of positioning in environments lacking satellite signals and achieve cost-effective indoor positioning, this study aimed to develop an inexpensive indoor positioning program, in which the positions of users were calculated by pedestrian dead reckoning (PDR) using the built-in accelerometer and gyroscope in a mobile phone. In addition, the corner and linear calibration points were established to correct the positions with the map assistance. Distance, azimuth, and rotation angle detections were conducted for analyzing the indoor positioning results. The results revealed that the closure accuracy of the PDR positioning was enhanced by more than 90% with a root mean square error of 0.6 m after calibration. Ninety-four percent of the corrected PDR positioning results exhibited errors of <1 m, revealing a desk-level positioning accuracy. Accordingly, this study successfully combined mobile phone sensors with map assistance for improving indoor positioning accuracy.

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