Terrestrial Mobile Mapping: photogrammetric simulator

Taglioretti, Cinzia; Manzino, Ambrogio

doi:10.5194/isprsarchives-xl-3-333-2014

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Therefore, in order to obtain a good computation of the solution and since it does not imply an improvement in the redundancy distribution, points in the central zone are not required although a homogeneous distribution is preferable in the other zones. In fact, contrastingly to a simulated case study (Taglioretti and Manzino, 2014), in a real case TPs are not well distributed in the overlap area (as shown in Figure 6): the TPs are not evenly distributed in a regular or homogeneous manner, but are usually located in a specific portion of the overlap area ( Figure 6). Extraction of the maximum 18…”

Section: Partition Of the Overlap Area Between The Framesmentioning

confidence: 65%

On Outlier Detection in a Photogrammetric Mobile Mapping Dataset

Taglioretti

Manzino

Bellone

et al. 2015

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

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ABSTRACT:Various types of technology are used for Terrestrial Mobile Mapping (TMM) such as IMU, cameras, odometers, laser scanner etc., which are integrated in order to determine the attitude and the position of the vehicle in use, especially in the absence of GNSS signal i.e. in an urban canyon. The aim of this study is to use only photogrammetric measurements obtained with a low cost camera (with a reduced focal length and small frames) located on the vehicle, in order to improve the quality of TMM solution in the absence of a GNSS signal. It is essential to have good quality frames in order to solve this problem. In fact it is generally quite easy to extract a large number of common points between the frames (the so-called 'tie points'), but this does not necessarily imply the goodness of the matching quality, which might be uncorrected due to the presence of obstacles that may occlude the camera sight. The Authors used two different methods for solving the problem of the presence of outliers: RANSAC and the Forward Search. In this article the Authors show the results obtainable with good quality frames (frames without occlusions) and under difficult conditions that simulate better reality.

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Section: Partition Of the Overlap Area Between The Framesmentioning

confidence: 65%

On Outlier Detection in a Photogrammetric Mobile Mapping Dataset

Taglioretti

Manzino

Bellone

et al. 2015

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

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“…Obviously it was possible using a software able to simulate the situation represented by the case study (Taglioretti and Manzino, 2014), but in order to achieve this goal, the authors initially use a commercial software (frequently used in the field of photogrammetric research), able automatically to discover a lot of tie points (TP) between the frames and to compute the orientation between successive frames. The latest goal is the introduction of attitude parameters between the state parameters for the estimation of the camera coordinates with the respective errors of measure.…”

Section: Resultsmentioning

confidence: 99%

Odometry and Low-Cost Sensor Fusion in TMM Dataset

Manzino

Taglioretti

2016

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

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ABSTRACT:The aim of this study is to identify the most powerful motion model and filtering technique to represent an urban terrestrial mobile mapping (TMM) survey and ultimately to obtain the best representation of the car trajectory. The authors want to test how far a motion model and a more or less refined filtering technique could bring benefits in the determination of the car trajectory. To achieve the necessary data for the application of the motion models and the filtering techniques described in the article, the authors realized a TMM survey in the urban centre of Turin by equipping a vehicle with various instruments: a low-cost action-cam also able to record the GPS trace of the vehicle even in the presence of obstructions, an inertial measurement system and an odometer. The results of analysis show in the article indicate that the Unscented Kalman Filter (UKF) technique provides good results in the determination of the vehicle trajectory, especially if the motion model considers more states (such as the positions, the tangential velocity, the angular velocity, the heading, the acceleration). The authors also compared the results obtained with a motion model characterized by four, five and six states. A natural corollary to this work would be the introduction to the UKF of the photogrammetric information obtained by the same camera placed on board the vehicle. These data would permit to establish how photogrammetric measurements can improve the quality of TMM solutions, especially in the absence of GPS signals (like urban canyons).

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“…This approach has long been implemented to evaluate the expected performances of image acquisition strategies as well as processing algorithms for different mapping methods based on remote sensing data, i.e., photogrammetry [97], lidar [98], and radar methods [99,100]. More recently it has also been applied to terrestrial mobile mapping [101]. Although this approach may suffer from a lack of realism that could create biases in the analysis, it has several advantages such as the possibility to vary the experimental conditions:…”

Section: Simulation-based Dem Production Methods Validationmentioning

confidence: 99%

Digital Elevation Model Quality Assessment Methods: A Critical Review

2020

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Digital elevation models (DEMs) are widely used in geoscience. The quality of a DEM is a primary requirement for many applications and is affected during the different processing steps, from the collection of elevations to the interpolation implemented for resampling, and it is locally influenced by the landcover and the terrain slope. The quality must meet the user’s requirements, which only make sense if the nominal terrain and the relevant resolution have been explicitly specified. The aim of this article is to review the main quality assessment methods, which may be separated into two approaches, namely, with or without reference data, called external and internal quality assessment, respectively. The errors and artifacts are described. The methods to detect and quantify them are reviewed and discussed. Different product levels are considered, i.e., from point cloud to grid surface model and to derived topographic features, as well as the case of global DEMs. Finally, the issue of DEM quality is considered from the producer and user perspectives.

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Terrestrial Mobile Mapping: photogrammetric simulator

Cited by 3 publications

References 7 publications

On Outlier Detection in a Photogrammetric Mobile Mapping Dataset

On Outlier Detection in a Photogrammetric Mobile Mapping Dataset

Odometry and Low-Cost Sensor Fusion in TMM Dataset

Digital Elevation Model Quality Assessment Methods: A Critical Review

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