Publications by Jan Skaloud | EPFL Graph Search

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The proliferation of (low-cost) sensors provokes new challenges in data fusion. This is related to the correctness of stochastic characterization that is a prerequisite for optimal estimation of parameters from redundant observations. Different (statistical) methods were developed to estimate parameters of complex stochastic models. To cite a few, there is the maximum likelihood approach estimated via the so-called EM algorithm as well as a linear regression approach based on the log-log-representation of a

Proper modeling of stochastic errors in inertial sensors plays a crucial role in the achievable quality of GNSS-INS integration especially with low-cost inertial sensors. Generalized Method of Wavelet Moments (GMWM) can model the underlying process for suc ...

The evolution of airborne mapping witnesses the introduction of hybrid lidar-camera systems to enhance data collection, i.e. to obtain simultaneously high-density point-cloud and texture. Yet, the common adjustment of both optical data streams is a nontriv ...

This study highlights the benefits of precise aerial position and attitude control in the context of mapping with Micro Aerial Vehicles (MAVs). Accurate mapping with MAVs is gaining importance in applications such as corridor mapping, road and pipeline ins ...

Stochastic modeling is a challenging task for low-cost sensors whose errors can have complex spectral structures. This makes the tuning process of the INS/GNSS Kalman filter often sensitive and difficult. For example, first-order Gauss–Markov processes are ...

The task of inertial sensor calibration has required the development of various techniques to take into account the sources of measurement error coming from such devices. The calibration of the stochastic errors of these sensors has been the focus of incre ...

Tightly-coupled sensor orientation, i.e. the simultaneous processing of temporal (GNSS and raw inertial) and spatial (image and lidar) constraints in a common adjustment, has demonstrated significant improvement in the quality of attitude determination wit ...

Systematic and random errors inherently present in all inertial sensors contribute to the long-term divergence of the navigation solution of an Inertial Navigation System (INS). To keep such long-term divergence under control while taking advantage of their excellent short-term performance, an INS is often combined with information from other navigation aids. However, the improvements due to external aiding are limited by the type, accuracy and update period of the information provided by the aid(s). In the

Modeling and estimation of gyroscope and accelerometer errors is generally a very challenging task, especially for low-cost inertial MEMS sensors whose systematic errors have complex spectral structures. Consequently, identifying correct error-state parameters in a INS/GNSS Kalman filter / smoother becomes difficult when several processes are superimposed. In such situations, the classical identification approach via Allan Variance (AV) analyses fails due to the difficulty of separating the error-processes

The diversity of road applications and intelligent transportation systems (ITS) makes the definition of positioning integrity a real challenge because the requirements are changing from one application to another. Even within liability or safety critical applications, the role of positioning may vary if the application layer requires a position at specific location (e.g. emergency call) or a series of positions along a vehicle’s trajectory (e.g. transport of dangerous goods). -- This paper is focusing on th

t is well known that accurate aerial position and attitude control is beneficial for image orientation in airborne photogrammetry. The aerial control is traditionally obtained by Kalman filtering/smoothing inertial and GNSS observations prior to the bundle ...

Light detection and ranging (LiDAR) has demonstrated its capabilities as a prominent technique for the direct acquisition of high density and accurate topographic information. To achieve the potential accuracy of such systems, a rigorous system calibration ...

Proposed 50 years ago for studying stability of oscillators, Allan Variance (AV) was accepted by IEEE as a standard for characterizing behavior of sensors. However, the inverse mapping, i.e. the estimation of noise-parameters from Allan Variance is less straightforward and considerably more difficult procedure. The parameters that can be reliably estimated via a linear regression on the AV log-log plots are limited to few possible models and this approach has recently been proven inconsistent. Alternative e

We present an algorithm for determining the nature of stochastic processes together with its parameters based on the analysis of time series of inertial errors. The algorithm is suitable mainly (but not only) for situations when several stochastic processes are superposed. In such cases, classical approaches based on the analysis of Allan variance or PSD are likely to fail due to the difficulty of separating the underlying error-processes in the spectral domain. The developed alternative is based on the rec

We present an algorithm for determining the nature of stochastic processes and their parameters based on the analysis of time series of inertial errors. The algorithm is suitable mainly (but not only) for situations where several stochastic processes are s ...

Inertial sensors are increasingly being employed in different types of applications. The reduced cost and the extremely small size makes them the number-one-choice in miniature embedded devices like phones, watches, and small unmanned aerial vehicles. The ...

Abstract. Tree instance segmentation of airborne laser scanning (ALS) data is of utmost importance for forest monitoring but remains challenging due to variations in the data caused by factors such as sensor resolution, vegetation state at acquisition time, terrain characteristics, etc. Moreover, obtaining a sufficient amount of precisely labeled data to train fully supervised instance segmentation methods is expensive. To address these challenges, we propose a weakly supervised approach where labels of an

The quality of the laser point cloud georeferencing in airborne laser scanning missions is largely related to the quality of the GPS solution. The latter is obtained by post- processing the differential carrier-phase measurements in order to reach the required accuracy. This implies that errors or unacceptable quality in the gathered data that cause problems for the ambiguity resolution in the post- processing step are detected much later. The objective of this thesis is to investigate new concepts of GPS d

We revisit the concept of scalar gravity ano- maly determination by an airborne strapdown INS - GNSS system. We built on the previously investigated concepts (mainly within 1995-2005 period) while trying to decrease the error spectrum of the system caused by accelerometer biases at lower frequencies and GNSS- position/velocity noise at shorter wavelengths. We claim that the determination of the random long-term ac- celerometer bias is possible through combination of - GRACE + GOCE data that provide an unbia

Recent innovations in LiDAR sensors and machine learning have significantly improved our ability to capture and label powerline infrastructure in 3D. By leveraging high-density LiDAR data acquired by drones, small yet critical elements such as insulators, attachment points, conductor and guard cables can be detected and classified with better accuracy. However, simply annotating point clouds does not directly translate to ready-to-use geographic database elements, prompting a need for reliable vectorization

Jan Skaloud

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Parameter Determination of Sensor Stochastic Models under Covariate Dependency

Optimal Stochastic Sensor Error Modeling based on Actual Impact on Quality of GNSS-INS Integrated Navigation

On the Perspectives of Image-to-Lidar Constraints in Dynamic Network Optimisation

Applicability Of New Approaches Of Sensor Orientation To Micro Aerial Vehicles

Constrained expectation-maximization algorithm for stochastic inertial error modeling: study of feasibility

Wavelet-Based Moment-Matching Techniques for Inertial Sensor Calibration

Airborne sensor fusion: Expected accuracy and behavior of a concurrent adjustment

Detection and filtering of short-term (1/f) noise in inertial sensors

A Framework for Inertial Sensor Calibration Using Complex Stochastic Error Models

Position Accuracy with Redundant MEMS IMU for Road Applications

Bundle adjustment with raw inertial observations

Automated approach for rigorous light detection and ranging system calibration without preprocessing and strict terrain coverage requirements

Beyond Allan Variance - GMWM Framework For Sensor Calibration

An algorithm for automatic inertial sensor calibration

Automatic Identification and Calibration of Stochastic Parameters in Inertial Sensors

An overview of a new sensor calibration platform

Weakly-Supervised Learning for Tree Instances Segmentation in Airborne Lidar Point Clouds

GPS/INS Integrity in Airborne Mapping

Progress in airborne gravimetry by combining strapdown inertial and new satellite observations via dynamic networks

Automated Vectorization of Classified LiDAR Data for Powerline Infrastructure Modeling