Artoolkitplus for pose tracking on mobile devices

By Daniel Wagner, Dieter Schmalstieg. 2007

344 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0812270d7be379c501f54041d3b7c689100b45e9

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Citations in the corpus

Mobile Augmented Reality Survey: From Where We Are to Where We Go (2017, methodology): “Schmalstieg et al. [28] constructed a MAR-based city navigation system.”
Real-Time, Cloud-Based Object Detection for Unmanned Aerial Vehicles (2017, methodology): “We use a visual marker detection library, ArtoolkitPlus, in our update step in order to get accurate and robust absolution position estimation results within the test environment [31].”
Pose Estimation for Augmented Reality: A Hands-On Survey (2016, unknown): “A very efﬁcient solution for planar target pose estimation is considered in [115] and has been used in ARToolkit+ [145].”
PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision (2012, methodology): “3.1 ARTK+ localization We use a localization test bed that uses markers with an adapted implementation of ARToolkit+ (Wagner and Schmalstieg 2007) for the localization.”
Vision-based autonomous mapping and exploration using a quadrotor MAV (2012, unknown): “Meier et al. [19] pushed the technology further by demonstrating autonomous takeoff, landing, hovering, and waypoint following over a much larger space using numerous ARToolKitPlus [24] markers laid on the ground.”
Pose tracking from natural features on mobile phones (2008, background): “This port later evolved into the ARToolKitPlus tracking library [27].”
Real-Time Detection and Tracking for Augmented Reality on Mobile Phones (2010, background): “This port later evolved into the AR- ToolKitPlus tracking library [18].”
The KIT object models database: An object model database for object recognition, localization and manipulation in service robotics (2012, methodology): “To do this an extrinsic calibration is carried out using the ARToolkit+ (Wagner and Schmalstieg 2007) by positioning a marker on the turntable.”
Automatic generation and detection of highly reliable fiducial markers under occlusion (2014, methodology): “However, the minimum distance in AR- ToolKitPlus considering all the BCH markers is 2, which is a low value in comparison to our method, or ARTag.”
Speeded up detection of squared fiducial markers (2018, methodology): “When compared to other approaches such as Chilitags, AprilTags, and ArToolKit+, our method achieves even higher speedups.”
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-Less Objects (2017, methodology): “The extrinsic calibration was achieved using fiducial BCH-code markers from ARToolKitPlus [52].”
AprilTag 2: Efficient and robust fiducial detection (2016, methodology): “Surviving forks of the project include ARToolkitPlus [5] and Studierstube Tracker [6].”
A Survey of Augmented Reality (2015, methodology): “ARToolkitPlus and Studierstube ES ARToolkitPlus [Wagner and Schmalstieg, 2007] was a ground up rebuild of the original ARToolkit, focusing on performance optimization and various image processing”