Comparison of Two Image and Inertial Sensor Fusion Techniques for Navigation in Unmapped Environments

Taylor, Clark N.; Veth, Michael J.; Raquet, John; Miller, Mikel M.

doi:10.1109/taes.2011.5751236

Cited by 26 publications

(15 citation statements)

References 15 publications

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“…This approach requires a number of reference nodes (anchor nodes or landmarks) deployed at fixed locations as well as one or more mobile nodes (receiver). Another approach is to combine the information provided by Inertial Measurement Unit (IMU) and aiding sensors such as cameras (monocular, stereo and RGB-D) and LiDAR sensors (Leutenegger, 2013;Veth, 2011).…”

Section: Indoor Positioning and Mappingmentioning

confidence: 99%

“…Feature extraction and matching algorithms are commonly combined, with RANSAC procedures that aim to perform outlier detection and removal using solely image observations by means of position and attitude estimation (Hartley and Schaffalitzky, 2004;Nistér, 2003). Other approaches combine derived trajectories or inertial data to predict where a point feature should appear in the second image (Veth, 2011;Taylor, 2011).…”

Section: Indoor Positioning and Mappingmentioning

confidence: 99%

“…When a loop closure is detected and applied or a reference map is used to reduce positioning drift, visual odometry is known as SLAM. Alternatively, (Taylor, 2011) presents two strategies to use an IMU as a primary positioning sensor and to control the inertial drift with visual information during the filtering step. The two approaches are implemented using an Unscented Kalman Filter estimation method.…”

Section: Indoor Positioning and Mappingmentioning

confidence: 99%

See 2 more Smart Citations

Towards a Fast, Low-Cost Indoor Mapping and Positioning System For Civil Protection and Emergency Teams

Angelats

Navarro

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Civil protection and emergency teams work usually under very risky conditions that endanger their lives. One of the factors contributing to such risks is the lack of knowledge about their physical environment, especially when working indoors. Mapping and location indoor and outdoor technologies exist; for outdoors, very good levels of precision and accuracy may be obtained using offthe-shelf equipment; on the other side, and although good solutions for indoor environments are available, these require some extra pre-deployed infrastructure in the area to navigate, which is unacceptable in the case of emergency teams. It may be said, then, that no mature indoor + outdoor integrated solution providing the appropriate precision and accuracy for the purposes of emergency teams exist. In this paper, the assessment of a set of currently available sensors (IMUs, RGB-D cameras, GNSS receivers) and algorithms is presented to show that it is already possible to build such a solution relying on them -providing that appropriate (indoor) lightning and texture conditions exist.

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Section: Indoor Positioning and Mappingmentioning

confidence: 99%

Section: Indoor Positioning and Mappingmentioning

confidence: 99%

Section: Indoor Positioning and Mappingmentioning

confidence: 99%

See 1 more Smart Citation

Towards a Fast, Low-Cost Indoor Mapping and Positioning System For Civil Protection and Emergency Teams

Angelats

Navarro

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…On the one hand, imaging sensor performance has been a key enabling technology allowing for information-rich data acquisition. On the other hand, algorithmic developments, in particular, advances in computer vision, have highly automated the extraction of geometrical information, making it feasible to efficiently integrate it into navigation filters [19,20,21,22,23,24]. Broadly speaking, imaging sensors work in active and passive modes, based on whether they provide a signal to observe the object space or just sense some part of the spectrum.…”

Section: Image Based Pntmentioning

confidence: 99%

Multisensor Navigation Systems: A Remedy for GNSS Vulnerabilities?

et al. 2016

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Abstract-Space-based positioning, navigation and timing (PNT) technologies, such as the Global Navigation Satellite Systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years PNT information has become increasingly critical to the security, safety and prosperity of the World's population, and is now widely recognized as an essential element of the global information infrastructure.Due to its vulnerabilities and line-of-sight requirements GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity and reliability. A multi-sensor navigation approach offers an effective augmentation in GNSSchallenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs), barometers, magnetometers, odometers and digital compasses, have been used. However, recent trends have largely focused on image-based, terrain-based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multi-sensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever-growing numbers of users, the hybridization of a suite of different PNT solutions is required.

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“…These approaches are usually referred as visual odometry (VO) or Structure from Motion (SfM) in the robotics and computer vision community. Alternatively, (Taylor et al, 2011) presents two strategies to use an IMU as a primary positioning sensor and to control inertial drift with visual information during filtering step. The two approaches are implemented using an Unscented Kalman Filter estimation method.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Models for Trajectory Error Modelling in Urban Environments

Angelatsa

Parés

Colomina³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper tackles the first step of any strategy aiming to improve the trajectory of terrestrial mobile mapping systems in urban environments. We present an approach to model the error of terrestrial mobile mapping trajectories, combining deterministic and stochastic models. Due to urban specific environment, the deterministic component will be modelled with non-continuous functions composed by linear shifts, drifts or polynomial functions. In addition, we will introduce a stochastic error component for modelling residual noise of the trajectory error function. First step for error modelling requires to know the actual trajectory error values for several representative environments. In order to determine as accurately as possible the trajectories error, (almost) error less trajectories should be estimated using extracted nonsemantic features from a sequence of images collected with the terrestrial mobile mapping system and from a full set of ground control points. Once the references are estimated, they will be used to determine the actual errors in terrestrial mobile mapping trajectory. The rigorous analysis of these data sets will allow us to characterize the errors of a terrestrial mobile mapping system for a wide range of environments. This information will be of great use in future campaigns to improve the results of the 3D points cloud generation. The proposed approach has been evaluated using real data. The data originate from a mobile mapping campaign over an urban and controlled area of Dortmund (Germany), with harmful GNSS conditions. The mobile mapping system, that includes two laser scanner and two cameras, was mounted on a van and it was driven over a controlled area around three hours. The results show the suitability to decompose trajectory error with non-continuous deterministic and stochastic components.

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Comparison of Two Image and Inertial Sensor Fusion Techniques for Navigation in Unmapped Environments

Cited by 26 publications

References 15 publications

Towards a Fast, Low-Cost Indoor Mapping and Positioning System For Civil Protection and Emergency Teams

Towards a Fast, Low-Cost Indoor Mapping and Positioning System For Civil Protection and Emergency Teams

Multisensor Navigation Systems: A Remedy for GNSS Vulnerabilities?

Hybrid Models for Trajectory Error Modelling in Urban Environments

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