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Markus S. Mueller
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany

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Journal article
Published: 20 August 2019 in Remote Sensing
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Mobile Mapping is an efficient technology to acquire spatial data of the environment. The spatial data is fundamental for applications in crisis management, civil engineering or autonomous driving. The extrinsic calibration of the Mobile Mapping System is a decisive factor that affects the quality of the spatial data. Many existing extrinsic calibration approaches require the use of artificial targets in a time-consuming calibration procedure. Moreover, they are usually designed for a specific combination of sensors and are, thus, not universally applicable. We introduce a novel extrinsic self-calibration algorithm, which is fully automatic and completely data-driven. The fundamental assumption of the self-calibration is that the calibration parameters are estimated the best when the derived point cloud represents the real physical circumstances the best. The cost function we use to evaluate this is based on geometric features which rely on the 3D structure tensor derived from the local neighborhood of each point. We compare different cost functions based on geometric features and a cost function based on the Rényi quadratic entropy to evaluate the suitability for the self-calibration. Furthermore, we perform tests of the self-calibration on synthetic and two different real datasets. The real datasets differ in terms of the environment, the scale and the utilized sensors. We show that the self-calibration is able to extrinsically calibrate Mobile Mapping Systems with different combinations of mapping and pose estimation sensors such as a 2D laser scanner to a Motion Capture System and a 3D laser scanner to a stereo camera and ORB-SLAM2. For the first dataset, the parameters estimated by our self-calibration lead to a more accurate point cloud than two comparative approaches. For the second dataset, which has been acquired via a vehicle-based mobile mapping, our self-calibration achieves comparable results to a manually refined reference calibration, while it is universally applicable and fully automated.

ACS Style

Markus Hillemann; Martin Weinmann; Markus S. Mueller; Boris Jutzi. Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features. Remote Sensing 2019, 11, 1955 .

AMA Style

Markus Hillemann, Martin Weinmann, Markus S. Mueller, Boris Jutzi. Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features. Remote Sensing. 2019; 11 (16):1955.

Chicago/Turabian Style

Markus Hillemann; Martin Weinmann; Markus S. Mueller; Boris Jutzi. 2019. "Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features." Remote Sensing 11, no. 16: 1955.

Journal article
Published: 13 February 2018 in Drones
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The navigation of Unmanned Aerial Vehicles (UAVs) nowadays is mostly based on Global Navigation Satellite Systems (GNSSs). Drawbacks of satellite-based navigation are failures caused by occlusions or multi-path interferences. Therefore, alternative methods have been developed in recent years. Visual navigation methods such as Visual Odometry (VO) or visual Simultaneous Localization and Mapping (SLAM) aid global navigation solutions by closing trajectory gaps or performing loop closures. However, if the trajectory estimation is interrupted or not available, a re-localization is mandatory. Furthermore, the latest research has shown promising results on pose regression in 6 Degrees of Freedom (DoF) based on Convolutional Neural Networks (CNNs). Additionally, existing navigation methods can benefit from these networks. In this article, a method for GNSS-free and fast image-based pose regression by utilizing a small Convolutional Neural Network is presented. Therefore, a small CNN (SqueezePoseNet) is utilized, transfer learning is applied and the network is tuned for pose regression. Furthermore, recent drawbacks are overcome by applying data augmentation on a training dataset utilizing simulated images. Experiments with small CNNs show promising results for GNSS-free and fast localization compared to larger networks. By training a CNN with an extended data set including simulated images, the accuracy on pose regression is improved up to 61.7% for position and up to 76.0% for rotation compared to training on a standard not-augmented data set.

ACS Style

Markus S. Mueller; Boris Jutzi. UAS Navigation with SqueezePoseNet—Accuracy Boosting for Pose Regression by Data Augmentation. Drones 2018, 2, 7 .

AMA Style

Markus S. Mueller, Boris Jutzi. UAS Navigation with SqueezePoseNet—Accuracy Boosting for Pose Regression by Data Augmentation. Drones. 2018; 2 (1):7.

Chicago/Turabian Style

Markus S. Mueller; Boris Jutzi. 2018. "UAS Navigation with SqueezePoseNet—Accuracy Boosting for Pose Regression by Data Augmentation." Drones 2, no. 1: 7.