Ismael Colomina

Noise reduction and estimation in multiple micro-electro-mechanical inertial systems

This research studies the reduction and the estimation of the noise level within a redundant configuration of low-cost (MEMS-type) inertial measurement units (IMUs). Firstly, independent observations between units and sensors are assumed and the theoretical decrease in the system noise level is analyzed in an experiment with four MEMS-IMU triads. Then, more complex scenarios are presented in which the noise level can vary in time and for each sensor. A statistical method employed for studying the volatility of financial markets (GARCH) is adapted and tested for the usage with inertial data. This paper demonstrates experimentally and through simulations the benefit of direct noise estimation in redundant IMU setups.

ATENEA: Advanced techniques for deeply integrated GNSS/INS/LiDAR navigation

The ATENEA (Advanced Techniques for Navigation Receivers and Applications) project aims to join deeply integrated GNSS/INS receiver architectures and LIDAR techniques to provide an advanced navigation solution. The approach is suitable for a wide range of surveying applications in difficult environments, being Urban Mapping selected as reference case. ATENEA tackles the most challenging issues of this type of applications, showing how the use of Galileo signals, integrated positioning and observable processing can in one shot solve the more severe technical issues (robustness and continuity), increase accuracy and drastically reduce the system cost. The goal of the ATENEA project is to develop an advanced technology concept for seamless navigation at the cm-level regardless of the environment.

Dynamic dependent IMU stochastic modeling for enhanced INS/GNSS navigation

INS/GNSS integration is a well known technique in navigation and, in general, in time-Position-Velocity-Attitude (tPVA) trajectory determination. It is also known, that INS and GNSS are complementary and that the key to their integration is the correct calibration of the Inertial Measurement Unit (IMU) sensors, of the IMUs Inertial Sensor Assembly (ISA) system parameters (axes misalignments and center of navigation), and of the IMU-to-GNSS system parameters (eccentricity vectors). Calibration is performed both at the manufacturers facilities prior to shipping and “on-the-job,” i.e., together with navigation. Classical INS calibration has two parts: INS error modeling and actual INS calibration once an INS error model is available (also called INS calibration model) either at the manufaturers or on-the-job. INS error modeling is about understanding the error features — systematic and random — of an IMU and its result uses to be dynamic models — Stochastic Differential Equations (SDE) and white noise amplitude — of the main error sources like biases, scale factors and misalignment angles. INS calibration is about solving for the unknowns (states) of the SDE calibration model together with the unknown states of the SDE of motion. The quality of INS calibration is dependent on the mission dynamics. In classical INS/GNSS navigation, the INS calibration model is dynamics independent; i.e., SDEs formulas and white noise figures are derived following a number of established procedures and then kept fixed. However, it can be shown, that the error features and the INS error calibration model of an IMU change from mission to mission or, more to the point, from one to another type of dynamics. It is therefore natural, to investigate the relationship between IMU errors and motion dynamics and, once this is understood, use this knowledge to improve the performance of INS/GNSS tPVA trajectory determination. In this paper we present a method for IMU error analysis and its first results. It consists of using a reference IMU (REF) together with the IMU under test (IUT) for the simultaneous measurement of REF and ITU signals. First, we show how the sensor random behavior changes with dynamics for tactical and low-cost grade IMUs with respect a navigation grade one. Second, we introduce a procedure to analyze the error of the dynamic dat. The procedure is based on inertial data analysis tools as the Allan Variance applied to the errors of the target IMU with respect to the reference IMU.

GNSS/INS/LiDAR integration in urban environment: Algorithm description and results from ATENEA test campaign

The ATENEA (Advanced Techniques for Navigation Receivers and Applications) project aims to join deeply integrated GNSS/INS receiver architectures and LIDAR techniques to provide an advanced navigation solution. The approach is suitable for a wide range of surveying applications in difficult environments, being Urban Mapping selected as reference case. This paper presents the integrated GNSS+INS+LiDAR navigation filter, and detailed results of the test campaign carried out using both synthetic and real data. These results validate the ATENEA concept and open the possibilities of using such system for reliable navigation in urban environments.

ENCORE: Enhanced code Galileo receiver for land management applications in Brazil

Taking benefit of the new Galileo ranging signals, the ENCORE (Enhanced Code Galileo Receiver) project aims to develop a low-cost Land Management Application to cover needs of the Brazilian market in terms of geo-referencing and rural/urban cadastre, using a low-cost Enhanced Galileo Code Receiver as baseline. Land management applications require precision and accuracy levels from a few to several decimetres that are under-met with current pseudorange-based receiver and over-met with phase observations. This situation leads either to a waste of resources, or to lack of accuracy. In this project, it is proposed to fill this gap using the new possibilities of the Galileo ranging signals, in particular E5 AltBOC and E1 CBOC. This approach reduces the cost of the end-user solution, helping the rapid penetration of Galileo technology outside Europe

Editorial—Best of "ISPRS Hannover Workshop 2017"

Sensor calibration, image orientation, object extraction and scene understanding from images and image sequences are important research topics in Photogrammetry, Remote Sensing, Computer Vision and Geoinformation Science, the areas of interest of the International Society for Photogrammetry and Remote Sensing (ISPRS). Within these areas, both geometry and semantics play an important role, and high quality results require appropriate handling of all these aspects. While individual algorithms differ according to the imaging geometry and the employed sensors and platforms, all mentioned aspects need to be integrated in a suitable workflow to solve most real-world problems.

First Results of a Tandem Terrestrial-Unmanned Aerial mapKITE System with Kinematic Ground Control Points for Corridor Mapping

In this article, we report about the first results of the mapKITE system, a tandem terrestrial-aerial concept for geodata acquisition and processing, obtained in corridor mapping missions. The system combines an Unmanned Aerial System (UAS) and a Terrestrial Mobile Mapping System (TMMS) operated in a singular way: real-time waypoints are computed from the TMMS platform and sent to the UAS in a follow-me scheme. This approach leads to a simultaneous acquisition of aerial-plus-ground geodata and, moreover, opens the door to an advanced post-processing approach for sensor orientation. The current contribution focuses on analysing the impact of the new, dynamic Kinematic Ground Control Points (KGCPs), which arise inherently from the mapKITE paradigm, as an alternative to conventional, costly Ground Control Points (GCPs). In the frame of a mapKITE campaign carried out in June 2016, we present results entailing sensor orientation and calibration accuracy assessment through ground check points, and precision and correlation analysis of self-calibration parameters’ estimation. Conclusions indicate that the mapKITE concept eliminates the need for GCPs when using only KGCPs plus a couple of GCPs at each corridor end, achieving check point horizontal accuracy of μ E , N ≈ 1.7 px (3.4 cm) and μ h ≈ 4.3 px (8.6 cm). Since obtained from a simplified version of the system, these preliminary results are encouraging from a future perspective.

HRIGI - High-resolution earth imaging for geospatial information CMRT - City models, roads and traffic ISA - Image Sequence analysis EuroCOW - European calibration and orientation workshop

Sensor calibration, image orientation, object extraction and scene understanding from images and image sequences are important research topics in Photogrammetry, Remote Sensing, Computer Vision and Geoinformation Science. Within these areas, both geometry and semantics play an important role, and high quality results require appropriate handling of all these aspects. While individual algorithms differ according to the imaging geometry and the employed sensors and platforms, all mentioned aspects need to be integrated in a suitable workflow to solve most real-world problems.

A Generic, Extensible Data Model for Trajectory Determination Systems.

Trajectory Determination Systems (TDS) use the measurements delivered by sensors to estimate trajectories. Many are the types of sensors that may be used already by such systems and new ones appear constantly in the market. Variety and fast change pace pose a problem for the maintenance of any kind of software system, including TDSs. Some data models and / or standards dealing with sensor data exist, but these are either too generic (and ambitious) or, on the contrary, targeted at very specific types of observations (as, for instance, GNSS.) This paper introduces a compact but complete, generic and extensible data model powerful enough as to deal with the kind of observations involved in trajectory determination and able to alleviate or even eliminate the software maintenance toll derived by constant changes in data sources. Two materialization of this data model, its file and network interfaces are briefly presented here as well as a portable C++ library implementing such interfaces.