System Design, Calibration and Performance Analysis of a Novel 360° Stereo Panoramic Mobile Mapping System

Blaser, Stefan; Nebiker, Stephan; Cavegn, Stefan

doi:10.5194/isprs-annals-iv-1-w1-207-2017

Cited by 11 publications

(18 citation statements)

References 18 publications

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“…The core challenge is to establish correspondence between the observations of the different cameras. This challenge can be tackled by moving the MCS in a field of control points, which are known in a reference frame (Blaser et al, 2017). For a method like this the control points have to be measured with an extra sensor like a tachymeter or a lasertracker.…”

Section: Related Workmentioning

confidence: 99%

UCalMiCeL – UNIFIED INTRINSIC AND EXTRINSIC CALIBRATION OF A MULTI-CAMERA-SYSTEM AND A LASERSCANNER

Hillemann

Jutzi²

2017

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

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ABSTRACT:Unmanned Aerial Vehicle (UAV) with adequate sensors enable new applications in the scope between expensive, large-scale, aircraftcarried remote sensing and time-consuming, small-scale, terrestrial surveyings. To perform these applications, cameras and laserscanners are a good sensor combination, due to their complementary properties. To exploit this sensor combination the intrinsics and relative poses of the individual cameras and the relative poses of the cameras and the laserscanners have to be known. In this manuscript, we present a calibration methodology for the Unified Intrinsic and Extrinsic Calibration of a Multi-Camera-System and a Laserscanner (UCalMiCeL). The innovation of this methodology, which is an extension to the calibration of a single camera to a line laserscanner, is an unifying bundle adjustment step to ensure an optimal calibration of the entire sensor system. We use generic camera models, including pinhole, omnidirectional and fisheye cameras. For our approach, the laserscanner and each camera have to share a joint field of view, whereas the fields of view of the individual cameras may be disjoint. The calibration approach is tested with a sensor system consisting of two fisheye cameras and a line laserscanner with a range measuring accuracy of 30mm. We evaluate the estimated relative poses between the cameras quantitatively by using an additional calibration approach for Multi-Camera-Systems based on control points which are accurately measured by a motion capture system. In the experiments, our novel calibration method achieves a relative pose estimation with a deviation below 1.8• and 6.4mm.

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Section: Related Workmentioning

confidence: 99%

UCalMiCeL – UNIFIED INTRINSIC AND EXTRINSIC CALIBRATION OF A MULTI-CAMERA-SYSTEM AND A LASERSCANNER

Hillemann

Jutzi²

2017

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

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“…The same mobile mapping system was upgraded by Blaser et al (2017) with two stereoscopic multi-head panoramic cameras. The stereo base is horizontally aligned in driving direction which enables large stereo baselines.…”

Section: Related Workmentioning

confidence: 99%

“…The stereo image processing workflow was also adopted from the solution by Blaser et al (2017). The goal was to obtain geospatial 3D images which consist of the corrected equidistant RGB images and the corresponding depth values, ideally for every pixel.…”

Section: Processing Workflowmentioning

confidence: 99%

“…The calibration software was adopted from the mobile mapping calibration system by Blaser et al (2017). It uses a test field calibration with bundle adjustment according to Ellum & ElSheimy (2002) and supports the equidistant camera model for fisheye cameras (Abraham & Förstner, 2005).…”

Section: Calibrationmentioning

confidence: 99%

“…Such systems can be equipped with stereoscopic panoramic cameras either in a vertical (Earthmine, 2014) or horizontal arrangement (Blaser et al, 2017) to enable full coverage of complex environments. With the trend in architecture and construction towards digital building design and construction progress control, the need for accurate and rapid mapping of indoor scenes in 3D is also rising.…”

Section: Introductionmentioning

confidence: 99%

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Design and Implementation of a Novel Portable 360° Stereo Camera System With Low-Cost Action Cameras

Holdener

Nebiker

Blaser

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The demand for capturing indoor spaces is rising with the digitalization trend in the construction industry. An efficient solution for measuring challenging indoor environments is mobile mapping. Image-based systems with 360° panoramic coverage allow a rapid data acquisition and can be processed to georeferenced 3D images hosted in cloud-based 3D geoinformation services. For the multiview stereo camera system presented in this paper, a 360° coverage is achieved with a layout consisting of five horizontal stereo image pairs in a circular arrangement. The design is implemented as a low-cost solution based on a 3D printed camera rig and action cameras with fisheye lenses. The fisheye stereo system is successfully calibrated with accuracies sufficient for the applied measurement task. A comparison of 3D distances with reference data delivers maximal deviations of 3 cm on typical distances in indoor space of 2-8 m. Also the automatic computation of coloured point clouds from the stereo pairs is demonstrated.

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Deep Learning-Based GNSS Network-Based Real-Time Kinematic Improvement for Autonomous Ground Vehicle Navigation

Kim

Bae

2019

Journal of Sensors

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Much navigation over the last several decades has been aided by the global navigation satellite system (GNSS). In addition, with the advent of the multi-GNSS era, more and more satellites are available for navigation purposes. However, the navigation is generally carried out by point positioning based on the pseudoranges. The real-time kinematic (RTK) and the advanced technology, namely, the network RTK (NRTK), were introduced for better positioning and navigation. Further improved navigation was also investigated by combining other sensors such as the inertial measurement unit (IMU). On the other hand, a deep learning technique has been recently evolving in many fields, including automatic navigation of the vehicles. This is because deep learning combines various sensors without complicated analytical modeling of each individual sensor. In this study, we structured the multilayer recurrent neural networks (RNN) to improve the accuracy and the stability of the GNSS absolute solutions for the autonomous vehicle navigation. Specifically, the long short-term memory (LSTM) is an especially useful algorithm for time series data such as navigation with moderate speed of platforms. From an experiment conducted in a testing area, the LSTM algorithm developed the positioning accuracy by about 40% compared to GNSS-only navigation without any external bias information. Once the bias is taken care of, the accuracy will significantly be improved up to 8 times better than the GNSS absolute positioning results. The bias terms of the solution need to be estimated within the model by optimizing the layers as well as the nodes each layer, which should be done in further research.

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System Design, Calibration and Performance Analysis of a Novel 360° Stereo Panoramic Mobile Mapping System

Cited by 11 publications

References 18 publications

UCalMiCeL – UNIFIED INTRINSIC AND EXTRINSIC CALIBRATION OF A MULTI-CAMERA-SYSTEM AND A LASERSCANNER

UCalMiCeL – UNIFIED INTRINSIC AND EXTRINSIC CALIBRATION OF A MULTI-CAMERA-SYSTEM AND A LASERSCANNER

Design and Implementation of a Novel Portable 360° Stereo Camera System With Low-Cost Action Cameras

Deep Learning-Based GNSS Network-Based Real-Time Kinematic Improvement for Autonomous Ground Vehicle Navigation

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