Modeling and Calibration of Inertial and Vision Sensors

Hol, Jeroen D.; Schön, Thomas B.; Gustafsson, Fredrik

doi:10.1177/0278364909356812

Cited by 67 publications

(58 citation statements)

References 27 publications

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“…These results are consistent with the results presented in Hol et al [41] and can be considered to have been resolved to an accuracy suitable for this application. The SfM algorithm was able to orient all of the frames from each of the four flights.…”

Section: 17supporting

confidence: 92%

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Development of a UAV-LiDAR System with Application to Forest Inventory

et al. 2012

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We present the development of a low-cost Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system and an accompanying workflow to produce 3D point clouds. UAV systems provide an unrivalled combination of high temporal and spatial resolution datasets. The TerraLuma UAV-LiDAR system has been developed to take advantage of these properties and in doing so overcome some of the current limitations of the use of this technology within the forestry industry. A modified processing workflow including a novel trajectory determination algorithm fusing observations from a GPS receiver, an Inertial Measurement Unit (IMU) and a High Definition (HD) video camera is presented. The advantages of this workflow are demonstrated using a rigorous assessment of the spatial accuracy of the final point clouds. It is shown that due to the inclusion of video the horizontal accuracy of the final point cloud improves from 0.61 m to 0.34 m (RMS error assessed against ground control). The effect of the very high density point clouds (up to 62 points per m 2 ) produced by the UAV-LiDAR system on the measurement of tree location, height and crown width are also assessed by performing repeat surveys over individual isolated trees. The standard deviation of tree height is shown to reduce from 0.26 m, when using data with a density of 8 points per m 2 , to 0.15 m when the higher density data was used.Improvements in the uncertainty of the measurement of tree location, 0.80 m to 0.53 m, and crown width, 0.69 m to 0.61 m are also shown.Remote Sens. 2012, 4 1520

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Section: 17supporting

confidence: 92%

“…A technique, outlined by Hol et al [41], which makes use of observations of the gravity vector made by both sensors was employed for this task. This procedure was repeated on 15 separate occasions to ensure that the correct boresight angles were found.…”

Section: Calibrationmentioning

confidence: 99%

Development of a UAV-LiDAR System with Application to Forest Inventory

et al. 2012

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“…The rover was fixed with a proper position and orientation in order to make the axes of its coordinate system have approximately the same orientation of those of the camera, and translation between their centers is mostly in a single direction (the vertical axis). Calibration of the camera-UWB rover system was performed similarly to the procedure described in [50]. The correction of camera positions can be applied once an estimate of the transformation from Σ photo to Σ uwb is available.…”

Section: Photogrammetric Reconstruction With the Uwb Positioning Systemmentioning

confidence: 99%

Performance Evaluation of Two Indoor Mapping Systems: Low-Cost UWB-Aided Photogrammetry and Backpack Laser Scanning

et al. 2018

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During the past dozen years, several mobile mapping systems based on the use of imaging and positioning sensors mounted on terrestrial (and aerial) vehicles have been developed. Recently, systems characterized by an increased portability have been proposed in order to enable mobile mapping in environments that are difficult to access for vehicles, in particular for indoor environments. In this work the performance of a low-cost mobile mapping system is compared with that of: (i) a state-of-the-art terrestrial laser scanning (TLS), considered as the control; (ii) a mobile mapping backpack system (Leica Pegasus), which can be considered as the state-of-the-art of commercial mobile mapping backpack systems. The aim of this paper is two-fold: first, assessing the reconstruction accuracy of the proposed low-cost mobile mapping system, based on photogrammetry and ultra-wide band (UWB) for relative positioning (and a GNSS receiver if georeferencing is needed), with respect to a TLS survey in an indoor environment, where the global navigation satellite system (GNSS) signal is not available; second, comparing such performance with that obtained with the Leica backpack. Both mobile mapping systems are designed to work without any control point, to enable an easy and quick survey (e.g., few minutes) and to be easily portable (relatively low weight and small size). The case study deals with the 3D reconstruction of a medieval bastion in Padua, Italy. Reconstruction using the Leica Pegasus backpack allowed obtaining a smaller absolute error with respect to the UWB-based photogrammetric system. In georeferenced coordinates, the root mean square (RMS) error was respectively 16.1 cm and 50.3 cm; relative error in local coordinates was more similar, respectively 8.2 cm and 6.1 cm. Given the much lower cost (approximately $6k), the proposed photogrammetric-based system can be an interesting alternative when decimetric reconstruction accuracy in georeferenced coordinates is sufficient.

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“…Several methods for camera-IMU calibration have been proposed in the past. A recent example is [7]. Such methods typically compute the full relative pose between IMU and camera (both translation and rotation).…”

Section: Related Workmentioning

confidence: 99%

“…No matter how carefully the IMU and camera are joined there will likely be some alignment error which could disturb the results [7].…”

Section: Relation Of Coordinate Framesmentioning

confidence: 99%

Why would i want a gyroscope on my RGB-D sensor?

Ovrén

Forssén

Törnqvist

2013

2013 IEEE Workshop on Robot Vision (WORV)

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Many RGB-D sensors, e.g. the Microsoft Kinect, use rolling shutter cameras. Such cameras produce geometrically distorted images when the sensor is moving. To mitigate these rolling shutter distortions we propose a method that uses an attached gyroscope to rectify the depth scans. We also present a simple scheme to calibrate the relative pose and time synchronization between the gyro and a rolling shutter RGB-D sensor.We examine the effectiveness of our rectification scheme by coupling it with the the Kinect Fusion algorithm. By comparing Kinect Fusion models obtained from raw sensor scans and from rectified scans, we demonstrate improvement for three classes of sensor motion: panning motions causes slant distortions, and tilt motions cause vertically elongated or compressed objects. For wobble we also observe a loss of detail, compared to the reconstruction using rectified depth scans.As our method relies on gyroscope readings, the amount of computations required is negligible compared to the cost of running Kinect Fusion.

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Modeling and Calibration of Inertial and Vision Sensors

Cited by 67 publications

References 27 publications

Development of a UAV-LiDAR System with Application to Forest Inventory

Development of a UAV-LiDAR System with Application to Forest Inventory

Performance Evaluation of Two Indoor Mapping Systems: Low-Cost UWB-Aided Photogrammetry and Backpack Laser Scanning

Why would i want a gyroscope on my RGB-D sensor?

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