A 3D pose estimator for the visually impaired

Hesch, Joel A.; Mirzaei, Faraz M.; Mariottini, Gian Luca; Roumeliotis, Stergios I.

doi:10.1109/iros.2009.5354060

Cited by 15 publications

(11 citation statements)

References 26 publications

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“…In our previous work [8], we presented a map-based indoor localization aid for the visually impaired using an IMU and a 2D laser scanner. Despite the novelty of our previous approach, it suffers from the same limitations of all map-based localization methods which include: (i) time and cost associated with acquiring the map, (ii) the system's inability to adapt to spatial layout changes, and (iii) the restriction of use to previously mapped areas.…”

Section: Related Workmentioning

confidence: 99%

“…To meet these objectives, we build upon our previous work [7], [8], and present an indoor Laser-aided Inertial Navigation System (L-INS) using an Inertial Measurement Unit (IMU) and a 2D laser scanner. Employing this sensor pair ensures feasibility of manufacturing a light-weight and compact sensor package that can be carried by a person, since a wide variety of small IMUs (e.g., Memsense nIMU) and compactsize 2D laser scanners (e.g., Hokuyo URG) are commercially available.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, as the person moves, the laser scanner's attitude changes which allows its scanning plane to intersect a variety of structural planes of the building (i.e., the walls, floor, and ceiling). If the structural planes were known a priori as in [7], [8], then the straight-line features could be directly matched with the known map. Unfortunately, in many cases in practice, a building map is not available in advance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Laser-Aided Inertial Navigation System (L-INS) for human localization in unknown indoor environments

Hesch

Mirzaei

Mariottini

et al. 2010

2010 IEEE International Conference on Robotics and Automation

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Laser-Aided Inertial Navigation System (L-INS) for human localization in unknown indoor environments

Hesch

Mirzaei

Mariottini

et al. 2010

2010 IEEE International Conference on Robotics and Automation

Self Cite

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“…There have been several attempts to obtain indoor localization through image matching techniques [10,6], but indoor environments are extremely challenging for this kind of application, and even if it is possible to recognize the shapes of pedestrians and objects through image processing techniques, the identification of similar ones is difficult for a computer vision-based system intended to monitor whole buildings [2]. In fact, the computational cost of high-dimensional feature extraction and processing is typically prohibitive for implementation in a cell-phone [8]. Consequently we have tried to focus on alternative ways for indoor localization.…”

Section: Location-based Approaches: Gps and Indoor Localizationmentioning

confidence: 99%

Opportunistic strategies for lightweight signal processing for body sensor networks

Seto

Martin

Yang

et al. 2010

Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments

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We present a mobile platform for body sensor networking based on a smartphone for lightweight signal processing of sensor mote data. The platform allows for local processing of data at both the sensor mote and smartphone levels, reducing the overhead of data transmission to remote services. We discuss how the smartphone platform not only provides the ability for wearable signal processing, but it allows for opportunistic sensing strategies, in which many of the onboard sensors and capabilities of modern smartphones may be collected and fused with body sensor data to provide environmental and social context. We propose that this can help refine data reduction at the local level. We describe three examples related to health and wellness, to which our system has been applied.

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“…A value between the two indicates that information is not independently available about the parameter, but exists about a linear combination of multiple parameters. Hesch et al (2009) shows that measurements of three orthogonal planes are sufficient to provide information about all six registration parameters of a range sensor. The ASRF is simulated in the three-plane environment to inspect its behaviour when information exists about all six registration parameters at some point throughout the measurement process.…”

Section: One-plane Test Environmentmentioning

confidence: 99%

Determining and verifying object pose from LiDAR measurements to support the perception needs of an autonomous excavator

Phillips¹

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This thesis is a study of how light detection and ranging, also known as LiDAR, can be used to meet perception requirements for field robot applications.LiDAR works by having an emitter produce a pulse of light along a known spatial direction. The distance or range to the world along that direction is determined by measuring the time it takes for reflections of the emitted light to be received at a collector. When this is done over multiple spatial directions, a cloud of points is produced that represent a sampling of the world. LiDAR is a mature technology and commercially available sensors are capable of producing point-clouds comprising up to 65,000 points over 360 degree fields of view, that are updated every 50 ms.This thesis is about how an autonomous machine can interpret meaning from these point-clouds in situations where the environment is complex and dynamic, where there is interaction with other agents leading to occlusion and where there is significant dust in the atmosphere.These conditions are systemic to open-cut mining environments, the domain where this thesis draws its motivation. In particular, the thesis uses as its primary example, the perception requirements for automation of a class of large mining excavators known as electric mining shovels. The work is specifically concerned with the problems of using point-clouds from machine mounted sensors to inform and verify knowledge of object position and orientation in the surrounds of these machines, for instance, the trucks being loaded.LiDAR is affected by dust and some see this a fatal flaw for mining robotics. The work starts with an exploration of the behaviour of a commercial LiDAR sensor in the presence of dust and finds that measurements are systematic and predictable. LiDAR sensors are found to exhibit four behaviours and it is shown how these behaviours can be understood from physics-based arguments. Several conclusions emerge, most notably where LiDAR measures dust, it does so to the leading edge of a dust-cloud rather than, say, as a random point within the cloud. This provides powerful insights into how better to use measurements from LiDAR when dust is present, and in particular suggests that dust can be treated like other forms of occlusion.A problem that must be solved to use LiDAR for machine perception is to establish from where each sensor views the world. This is sometimes called sensor registration and it amounts to finding where a sensor is positioned and how it is orientated. This is typically done by placing markers at known locations and computing the sensor registration from where they appear in the sensor data. For mining robot applications, the use of standard marker or artificial-feature-based approaches is infeasible. The thesis develops a method for sensor registration that overcomes this concern by utilizing the geometric structure of the terrain surrounding the autonomous vehicle platform. The method determines the i information content of registration parameters in measurements of the terrain, and update...

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A 3D pose estimator for the visually impaired

Cited by 15 publications

References 26 publications

A Laser-Aided Inertial Navigation System (L-INS) for human localization in unknown indoor environments

A Laser-Aided Inertial Navigation System (L-INS) for human localization in unknown indoor environments

Opportunistic strategies for lightweight signal processing for body sensor networks

Determining and verifying object pose from LiDAR measurements to support the perception needs of an autonomous excavator

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