The self-referenced DLR 3D-modeler

Strobl, Klaus H.; Mair, Elmar; Bodenmüller, Tim; Kielhöfer, Simon; Sepp, Wolfgang; Suppa, Michael; Burschka, Darius; Hirzinger, Gerd

doi:10.1109/iros.2009.5354708

Cited by 30 publications

(57 citation statements)

References 18 publications

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“…The variable visual parameters comprise camera orientation and potentially the robot's own motion. 1 Image features are salient areas of the image that are assumed to correspond to locally planar, textured patches in 3-D. Since they are measurable projections of the state of the system, they can enable pose tracking.…”

Section: A Active Visionmentioning

confidence: 99%

“…[1]. Next, Section III introduces the AM paradigm, which serves our purpose to present our novel development in Section IV as a best case scenario for AM.…”

Section: B Related Workmentioning

confidence: 99%

“…In Ref. [1] we presented an algorithm for efficient and accurate estimation of its motion from its own images. The novel development made it possible to abandon using inconvenient, expensive external positioning systems.…”

Section: D-modelermentioning

confidence: 99%

“…INTRODUCTION In Ref. [1] we presented the self-referenced DLR 3D-Modeler, which is a 3-D modeling device for close-range applications combining complementary sensors in a compact, generic way. A single 3-D modeling device is however rarely capable of creating complete 3-D models of a scene since the geometrical information gathered from a single vantage point is naturally limited.…”

mentioning

confidence: 99%

“…1 In Ref. [1] we bring forward a novel, hybrid feature drift prediction method that combines translational motion propagation with the rotational readings of an IMU. The method is easy to implement and allows for robust tracking with very high motion bandwidth.…”

mentioning

confidence: 99%

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Image-based pose estimation for 3-D modeling in rapid, hand-held motion

Strobl

Mair

Hirzinger

2011

2011 IEEE International Conference on Robotics and Automation

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Abstract-This work aims at accurate estimation of the pose of a close-range 3-D modeling device in real-time, at high-rate, and solely from its own images. In doing so, we replace external positioning systems that constrain the system in size, mobility, accuracy, and cost. At close range, accurate pose tracking from image features is hard because feature projections do not only drift in the face of rotation but also in the face of translation. Large, unknown feature drifts may impede real-time feature tracking and subsequent pose estimation-especially with concurrent operation of other 3-D sensors on the same computer. The problem is solved in Ref.[1] by the partial integration of readings from a backing inertial measurement unit (IMU). In this work we avoid using an IMU by improved feature matching: full utilization of the current state estimation (including structure) during feature matching enables decisive modifications of the matching parameters for more efficient tracking-we hereby follow the Active Matching paradigm.

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Section: A Active Visionmentioning

confidence: 99%

“…[1]. Next, Section III introduces the AM paradigm, which serves our purpose to present our novel development in Section IV as a best case scenario for AM.…”

Section: B Related Workmentioning

confidence: 99%

Section: D-modelermentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Image-based pose estimation for 3-D modeling in rapid, hand-held motion

Strobl

Mair

Hirzinger

2011

2011 IEEE International Conference on Robotics and Automation

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Loop Closing for Visual Pose Tracking during Close-Range 3-D Modeling

Strobl

2014

Advances in Visual Computing

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This work deals with the passive tracking of the pose of a close-range 3-D modeling device using its own high-rate images in realtime, concurrently with customary 3-D modeling of the scene by laser triangulation. Our former works in Refs. [1,2] successfully implemented visual pose tracking. Accuracy being a central requirement to 3-D modeling, however, here we note that accuracy can be further increased using a graph-based nonlinear optimization of the tracked pose by minimization of reprojection errors. Loop closures e.g. when having scanned all around the objects provide the opportunity to increase pose tracking and 3-D modeling accuracy. The sparse optimization is in the form of a hybrid, keyframe-based bundle adjustment algorithm on stereo keyframes, yielding rapid optimization of the whole trajectory and object mesh model within a second. The optimization is supported by the use of appearance-based SURF descriptors together with a bank of parallel three-point-perspective pose solvers.

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Single Camera Stereo System Using Prism and Mirrors

Somanath

Rohith

Kambhamettu

2010

Advances in Visual Computing

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Abstract. Stereo and 3D reconstruction are used by many applications such as object modeling, facial expression studies and human motion analysis. But synchronizing multiple high frame rate cameras require special hardware or sophisticated software solutions. In this paper, we propose a single camera stereo system by using a setup made of prism and mirrors. Our setup is cost effective, portable and can be calibrated similar to two camera stereo to obtain high quality 3D reconstruction. We demonstrate the application of the proposed system in dynamic 3D face expression capture, depth super-resolution in stereo video and general depth estimation.

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The self-referenced DLR 3D-modeler

Cited by 30 publications

References 18 publications

Image-based pose estimation for 3-D modeling in rapid, hand-held motion

Image-based pose estimation for 3-D modeling in rapid, hand-held motion

Loop Closing for Visual Pose Tracking during Close-Range 3-D Modeling

Single Camera Stereo System Using Prism and Mirrors

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