HeadSLAM - simultaneous localization and mapping with head-mounted inertial and laser range sensors

Cinaz, Burcu; Kenn, Holger

doi:10.1109/iswc.2008.4911575

Cited by 41 publications

(28 citation statements)

References 10 publications

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“…The problem of tracking the correct data association [16] as well as human indoor navigation and localization has recently become an active research field [5], [17], [18], [24]. A number of different sensors have been employed as well as different kinds of localization techniques have been used.…”

Section: Related Workmentioning

confidence: 99%

“…In the work of Toth et al [25], a prototype for pedestrian dead-reckoning and their general concept of sensor fusion is discussed. The HeadSLAM approach by Cinaz and Kenn [5] employs a laser scanner together with an IMU mounted on a helmet. They use the IMU sensor to project the laser scans into a horizontal plane in a global coordinate system and employ a variant of GMapping [14] for mapping.…”

Section: Related Workmentioning

confidence: 99%

“…This is performed by using motion templates for detecting door handling events and neural networks for detecting step activities as described in Section III. If an activity is detected and this activity is a stair step, we augment the odometry information of the current added nodes with our height estimate (lines [4][5][6][7][8]. If a currently detected activity is a door handling event, we calculate for each hypothesis Ω k j at depth k potential loop closure candidates C k j using a Dijkstra expansion starting from the corresponding current pose.…”

Section: Simultaneous Localization and Mappingmentioning

confidence: 99%

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Activity-Based Estimation of Human Trajectories

et al. 2012

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Abstract-We present a novel approach to incrementally determine the trajectory of a person in 3D based on its motions and activities in real-time. In our algorithm, we estimate the motions and activities of the user given the data obtained from a motion capture suit equipped with several inertial measurement units (IMUs). These activities include walking up and down staircases as well as opening and closing doors. We interpret the first two types of activities as motion constraints and door handling events as landmark detections in a graph-based simultaneous localization and mapping (SLAM) framework. Since we cannot distinguish between individual doors, we employ a multihypothesis tracking approach on top of the SLAM procedure to deal with the high data-association uncertainty. As a result, we are able to accurately and robustly recover the trajectory of the person. Additionally we present an algorithm to build approximate geometrical and the topological maps based on the estimated trajectory and detected activities. We evaluate our approach in practical experiments carried out with different subjects and in various environments.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Simultaneous Localization and Mappingmentioning

confidence: 99%

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Activity-Based Estimation of Human Trajectories

et al. 2012

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“…co-registration of scans to a common coordinate system, mobile laser scanning using the Simultaneous Localization and Mapping (SLAM) principle is the preferred method for experimental -e.g. (Cinaz and Kenn, 2008, Wen et al, 2016, Filgueira et al, 2016 -as well as commercial -Viametris iMS3D, NavVIS M3, ZEB1/ZEB REVO, Google Cartographer -systems.…”

Section: Introductionmentioning

confidence: 99%

Line Segmentation of 2d Laser Scanner Point Clouds for Indoor Slam Based on a Range of Residuals

Peter

Jafri

Vosselman

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Indoor mobile laser scanning (IMLS) based on the Simultaneous Localization and Mapping (SLAM) principle proves to be the preferred method to acquire data of indoor environments at a large scale. In previous work, we proposed a backpack IMLS system containing three 2D laser scanners and an according SLAM approach. The feature-based SLAM approach solves all six degrees of freedom simultaneously and builds on the association of lines to planes. Because of the iterative character of the SLAM process, the quality and reliability of the segmentation of linear segments in the scanlines plays a crucial role in the quality of the derived poses and consequently the point clouds. The orientations of the lines resulting from the segmentation can be influenced negatively by narrow objects which are nearly coplanar with walls (like e.g. doors) which will cause the line to be tilted if those objects are not detected as separate segments. State-of-the-art methods from the robotics domain like Iterative End Point Fit and Line Tracking were found to not handle such situations well. Thus, we describe a novel segmentation method based on the comparison of a range of residuals to a range of thresholds. For the definition of the thresholds we employ the fact that the expected value for the average of residuals of n points with respect to the line is σ/ √ n. Our method, as shown by the experiments and the comparison to other methods, is able to deliver more accurate results than the two approaches it was tested against.

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“…In the domain of SLAM for pedestrians, a few approaches have been proposed [6] [7][8] [9] [10]. In this contribution we will focus on FootSLAM [6] and present some estimates on how a mapping process based on it might scale for a largescale collaborative mapping process eventually encompassing most of our public indoor space.…”

Section: Introductionmentioning

confidence: 99%

Managing large-scale mapping and localization for pedestrians using inertial sensors

Puyol

Robertson

Angermann

2013

2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)

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Abstract-Pedestrian navigation in indoor environments without a pre-installed infrastructure still presents many challenges. There are different approaches that address the problem using prior knowledge about the environment when the building plans or similar are available. Since this is not always the case, a family of technologies based on the principle of Simultaneous Localization and Mapping (SLAM) has been proposed. In this paper we will present some estimates on how a mapping process based on FootSLAM -a form of SLAM for pedestrians -might scale for a large-scale collaborative effort eventually encompassing most of our public indoor space, where the mapping entities are humans.Our assumptions on pedestrian motion and area visiting rate together with calculations based on the computational requirements of pedestrian SLAM algorithms allow us to make estimates with regard to the feasibility, scalability and computational cost of wide-scale mapping of indoor areas by pedestrians.

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HeadSLAM - simultaneous localization and mapping with head-mounted inertial and laser range sensors

Cited by 41 publications

References 10 publications

Activity-Based Estimation of Human Trajectories

Activity-Based Estimation of Human Trajectories

Line Segmentation of 2d Laser Scanner Point Clouds for Indoor Slam Based on a Range of Residuals

Managing large-scale mapping and localization for pedestrians using inertial sensors

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