An approach to improve direct sensor orientation using the integration of photogrammetric and lidar datasets

Costa, Francisco Daniel de Oliveira; Mitishita, Edson Aparecido

doi:10.1080/01431161.2019.1580794

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…Equipped with the GNSS receiver (Trimble R8) and the inertial measurement unit (IMU, SPAN-CPT), the land vehicle was used as the testing carrier, and a three-axis open-loop gyroscope and three-axis MEMS accelerometers are included in the IMU. 4 Journal of Sensors…”

Section: Performance Evaluation and Analysismentioning

confidence: 99%

“…As the linear estimator of the mean and covariance, the Kalman filter has become the classic fusion algorithm in many fields [1,2], and it is implemented as the basic fusion method in the data processing with multiple sensors [3,4]. It has been proved that the Kalman filter is optimal only when the assumptions of Gaussian-distributed process or measurement noise hold [5].…”

Section: Introductionmentioning

confidence: 99%

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Modified Adaptive Fusion Scheme for Kalman Filter Based on the Hypothesis Test

Jiang

Liu

et al. 2022

Journal of Sensors

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In the literature, the fading factor was constructed to overcome the shortage of model uncertainties in the Kalman filter. However, the a priori covariance matrix might be inflated abnormally by the fading factor once the measurement is unreliable. Thus, the fading factor may become invalid, and this problem is rarely discussed and tested. In this paper, squares of the Mahalanobis distance are introduced as the judging index, and the fading factor or the covariance inflation factor is adopted conditionally according to the hypothesis testing result. Therefore, an adaptive filtering scheme based on the Mahalanobis distance is put forward for the systems with model uncertainties. The proposed algorithm is implemented with the actual data collected by the integration of the global navigation satellite system (GNSS) and the inertial navigation system and INS (inertial navigation system) integrated systems (INS). For the systems with model uncertainties, experimental results demonstrate that the influences of both the outlying measurements and model errors are controlled effectively with the proposed scheme.

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Section: Performance Evaluation and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modified Adaptive Fusion Scheme for Kalman Filter Based on the Hypothesis Test

Jiang

Liu

et al. 2022

Journal of Sensors

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“…With the advances in sensor and information fusion technology, the positioning and orientation system (POS) is explored for extrinsic parameter measurement and coordinate transformation in both land-based [ 26 ] and aerial photogrammetry fields [ 27 ]. This technique is known as direct sensor orientation (DSO) or direct georeferencing (DG).…”

Section: Introductionmentioning

confidence: 99%

Free-Surface Velocity Measurement Using Direct Sensor Orientation-Based STIV

et al. 2022

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Particle image velocimetry (PIV) is a quantitative flow visualization technique, which greatly improves the ability to characterize various complex flows in laboratory and field environments. However, the deployment of reference objects or ground control points (GCPs) for velocity calibration is still a challenge for in situ free-surface velocity measurements. By combining space-time image velocimetry (STIV) with direct sensor orientation (DSO) photogrammetry, a laser distance meter (LDM)-supported photogrammetric device is designed, to realize the GCPs-free surface velocity measurement under an oblique shooting angle. The velocity calibration with DSO is based on the collinear equation, while the lens distortion, oblique shooting angle, water level variation, and water surface slope are introduced to build an imaging measurement model with explicit physical meaning for parameters. To accurately obtain the in situ position and orientations of the camera utilizing the LDM and its embedded tilt sensor, the camera’s intrinsic parameters and relative position within the LDM are previously calibrated with a planar chessboard. A flume experiment is designed to evaluate the uncertainty of optical flow estimation and velocity calibration. Results show that the proposed DSO-STIV has good transferability and operability for in situ measurements. It is superior to propeller current meters and surface velocity radars in characterizing shallow free-surface flows; this is attributed to its non-intrusive, whole-field, and high-resolution features. In addition, the combined uncertainty of free-surface velocity measurement is analyzed, which provides an alternative solution for error assessment when comparing measurement failures.

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“…In (Zheng, et al, 2013;Huang, et al, 2018), the 3D points extracted from the images were constrained with the planes extracted from the LiDAR data through normal-shooting strategy. In (Costa, et al, 2017;Costa and Mitishita, 2019;Peng and Zhang, 2019), the line-segments or the corner points of the buildings, extracted from the LiDAR data through intersecting the point-cloud planes, were registered with those extracted from the images. In (Armenakis, et al, 2013), the author discussed the potential of directly using the planes extracted from both the images and the LiDAR data as the constraints for 3D registration.…”

Section: Introductionmentioning

confidence: 99%

Accurate Registration of Aerial Images and Als-Pointcloud via Automated Junction Matching and Planar Constraints

Wan

Zhang

Wang

et al. 2020

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

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Abstract. Accurate geometric registration of images and pointclouds is the key step of many 3D-reconstruction or 3D-sensing tasks. In this paper, a novel L-junction based approach is proposed for semi-automatic accurate registration of aerial images and the airborne laser scanning (ALS) point-cloud in urban areas. The approach achieves accurate registration by associating the LiDAR points with the local planes extracted via L-junction detection and matching from multi-view aerial images. An L-junction is an intersection of two line-segments. Through the forward intersection of multi-view corresponding L-junctions, an accurate local junction-plane can be obtained. In the proposed approach, L-junction is manually collected from one view on the flat object-surfaces like walls, roads, and roofs and then automatically matched to other views with the aid of epipolar-geometry and vanishing-point constraints. Then, a plane-constrained bundle block adjustment of the image-orientation parameters is conducted, where the LiDAR points are treated as reference data. The proposed approach was tested with two datasets collected in Guangzhou city and Ningbo city of China. The experimental results showed that the proposed approach had better accuracy than the closest-point based method. The horizontal/vertical registration RMS of the proposed approach reached 4.21cm/5.72cm in Guangzhou dataset and 4.46cm/4.34cm in Ningbo dataset, which was much less than the average LiDAR-point distance (over 25cm in both datasets) and was very close to the image GSDs (3.2cm in Guangzhou and 4.8cm in Ningbo) and the a-priori ranging accuracy of the ALS equipment (about 3cm).

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An approach to improve direct sensor orientation using the integration of photogrammetric and lidar datasets

Cited by 5 publications

References 16 publications

Modified Adaptive Fusion Scheme for Kalman Filter Based on the Hypothesis Test

Modified Adaptive Fusion Scheme for Kalman Filter Based on the Hypothesis Test

Free-Surface Velocity Measurement Using Direct Sensor Orientation-Based STIV

Accurate Registration of Aerial Images and Als-Pointcloud via Automated Junction Matching and Planar Constraints

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