Towards Palm-Size Autonomous Helicopters

muFly EU project started in 2006 with the idea to build an autonomous micro helicopter, comparable in size and weight to a small bird. Several scientific and technological objectives were identified. This spanned from system-level integration, high efficiency micro-actuation, highly integrated micro vision sensors and IMUs and also low processing power navigation algorithms. This paper shows how most of these objectives were reached, describing the approach and the role of each partner during the whole project. The paper describes also the technological developments achieved like the 80g, 17 cm micro robotic-helicopter, the 8g omnidirectional and steady-state laser scanner, the uIMU, the highly efficient micro motors, the high power-density fuel-cell and the successful graph-based navigation algorithm

A fast high-precision six-degree-of-freedom relative position sensor

Lasers are commonly used in high-precision measurement and profiling systems. Some laser measurement systems are based on interferometry principles, and others are based on active triangulation, depending on requirements of the application. This paper describes an active triangulation laser measurement system for a specific application wherein the relative position of two fixed, rigid mechanical components is to be measured dynamically with high precision in six degrees of freedom (DOF). Potential applications include optical systems with feedback to control for mechanical vibration, such as target acquisition devices with multiple focal planes. The method uses an array of several laser emitters mounted on one component. The lasers are directed at a reflective surface on the second component. The reflective surface consists of a piecewise-planar pattern such as a pyramid, or more generally a curved reflective surface such as a hyperbolic paraboloid. The reflected spots are sensed at 2-dimensional photodiode arrays on the emitter component. Changes in the relative position of the emitter component and reflective surface will shift the location of the reflected spots within photodiode arrays. Relative motion in any degree of freedom produces independent shifts in the reflected spot locations, allowing full six-DOF relative position determination between the two component positions. Response time of the sensor is limited by the read-out rate of the photodiode arrays. Algorithms are given for position determination with limits on uncertainty and sensitivity, based on laser and spot-sensor characteristics, and assuming regular surfaces. Additional uncertainty analysis is achievable for surface irregularities based on calibration data

Multi-wavelength, multi-beam, photonic based sensor for object discrimination and positioning

Over the last decade, substantial research efforts have been dedicated towards the development of advanced laser scanning systems for discrimination in perimeter security, defence, agriculture, transportation, surveying and geosciences. Military forces, in particular, have already started employing laser scanning technologies for projectile guidance, surveillance, satellite and missile tracking; and target discrimination and recognition. However, laser scanning is relatively a new security technology. It has previously been utilized for a wide variety of civil and military applications. Terrestrial laser scanning has found new use as an active optical sensor for indoors and outdoors perimeter security. A laser scanning technique with moving parts was tested in the British Home Office - Police Scientific Development Branch (PSDB) in 2004. It was found that laser scanning has the capability to detect humans in 30m range and vehicles in 80m range with low false alarm rates. However, laser scanning with moving parts is much more sensitive to vibrations than a multi-beam stationary optic approach. Mirror device scanners are slow, bulky and expensive and being inherently mechanical they wear out as a result of acceleration, cause deflection errors and require regular calibration. Multi-wavelength laser scanning represent a potential evolution from object detection to object identification and classification, where detailed features of objects and materials are discriminated by measuring their reflectance characteristics at specific wavelengths and matching them with their spectral reflectance curves. With the recent advances in the development of high-speed sensors and high-speed data processors, the implementation of multi-wavelength laser scanners for object identification has now become feasible. A two-wavelength photonic-based sensor for object discrimination has recently been reported, based on the use of an optical cavity for generating a laser spot array and maintaining adequate overlapping between tapped collimated laser beams of different wavelengths over a long optical path. While this approach is capable of discriminating between objects of different colours, its main drawback is the limited number of security-related objects that can be discriminated. This thesis proposes and demonstrates the concept of a novel photonic based multi-wavelength sensor for object identification and position finding. The sensor employs a laser combination module for input wavelength signal multiplexing and beam overlapping, a custom-made curved optical cavity for multi-beam spot generation through internal beam reflection and transmission and a high-speed imager for scattered reflectance spectral measurements. Experimental results show that five different laser wavelengths, namely 473nm, 532nm, 635nm, 670nm and 785nm, are necessary for discriminating various intruding objects of interest through spectral reflectance and slope measurements. Various objects were selected to demonstrate the proof of concept. We also demonstrate that the object position (coordinates) is determined using the triangulation method, which is based on the projection of laser spots along determined angles onto intruding objects and the measurement of their reflectance spectra using an image sensor. Experimental results demonstrate the ability of the multi-wavelength spectral reflectance sensor to simultaneously discriminate between different objects and predict their positions over a 6m range with an accuracy exceeding 92%. A novel optical design is used to provide additional transverse laser beam scanning for the identification of camouflage materials. A camouflage material is chosen to illustrate the discrimination capability of the sensor, which has complex patterns within a single sample, and is successfully detected and discriminated from other objects over a 6m range by scanning the laser beam spots along the transverse direction. By using more wavelengths at optimised points in the spectrum where different objects show different optical characteristics, better discrimination can be accomplished

3D Modelling from Real Data

The genesis of a 3D model has basically two definitely different paths. Firstly we can consider the CAD generated models, where the shape is defined according to a user drawing action, operating with different mathematical “bricks” like B-Splines, NURBS or subdivision surfaces (mathematical CAD modelling), or directly drawing small polygonal planar facets in space, approximating with them complex free form shapes (polygonal CAD modelling). This approach can be used for both ideal elements (a project, a fantasy shape in the mind of a designer, a 3D cartoon, etc.) or for real objects. In the latter case the object has to be first surveyed in order to generate a drawing coherent with the real stuff. If the surveying process is not only a rough acquisition of simple distances with a substantial amount of manual drawing, a scene can be modelled in 3D by capturing with a digital instrument many points of its geometrical features and connecting them by polygons to produce a 3D result similar to a polygonal CAD model, with the difference that the shape generated is in this case an accurate 3D acquisition of a real object (reality-based polygonal modelling). Considering only device operating on the ground, 3D capturing techniques for the generation of reality-based 3D models may span from passive sensors and image data (Remondino and El-Hakim, 2006), optical active sensors and range data (Blais, 2004; Shan & Toth, 2008; Vosselman and Maas, 2010), classical surveying (e.g. total stations or Global Navigation Satellite System - GNSS), 2D maps (Yin et al., 2009) or an integration of the aforementioned methods (Stumpfel et al., 2003; Guidi et al., 2003; Beraldin, 2004; Stamos et al., 2008; Guidi et al., 2009a; Remondino et al., 2009; Callieri et al., 2011). The choice depends on the required resolution and accuracy, object dimensions, location constraints, instrument’s portability and usability, surface characteristics, working team experience, project’s budget, final goal, etc. Although aware of the potentialities of the image-based approach and its recent developments in automated and dense image matching for non-expert the easy usability and reliability of optical active sensors in acquiring 3D data is generally a good motivation to decline image-based approaches. Moreover the great advantage of active sensors is the fact that they deliver immediately dense and detailed 3D point clouds, whose coordinate are metrically defined. On the other hand image data require some processing and a mathematical formulation to transform the two-dimensional image measurements into metric three-dimensional coordinates. Image-based modelling techniques (mainly photogrammetry and computer vision) are generally preferred in cases of monuments or architectures with regular geometric shapes, low budget projects, good experience of the working team, time or location constraints for the data acquisition and processing. This chapter is intended as an updated review of reality-based 3D modelling in terrestrial applications, with the different categories of 3D sensing devices and the related data processing pipelines

Transmitter Beam Bias Verification for Optical Satellite Data Downlinks with Open-Loop Pointing – the 3-OGS-Experiment

Optical free-space data downlinks from LEO satellites benefit considerably from reduced effort on the space segment, when a dedicated pointing mechanism and active tracking of a ground beacon can be avoided. Instead, the attitude of the satellite is dynamically determined from its star cameras and other sensors. Initial calibration for this technique requires recording of the spatial and temporal beam distribution on the Earth’s surface. We describe the measurement of the beam intensity on ground by the power detectors of three ground stations in parallel, exemplarily for one specific downlink. From this data we derive the instantaneous center of gravity of the beam spot, and its dynamic movement during the downlink. By comparison with the satellite’s own recorded attitude data and its error, the dynamic offset to be corrected on the satellite can be calculated, resulting in optimized pointing-control for future operational open-loop downlinks

3D modelling for surveying projects using unmanned arial vehicles (UAVs) and laser scanning

3D models that have been created from photogrammetry have some evident limitations. To create better, more complete 3D models, it is necessary to understand and reduce these limitations. The project aims to look at the effect of camera orientation and its effect on the overall accuracy of the project. Furthermore, it is proposed to reduce the inevitable gaps in the model by the use of terrestrial photogrammetry. The primary comparison of the model will be between the data captured from photogrammetry techniques and that of traditional style of surveying methods such as total station and terrestrial scanning. The research was conducted in late 2015 and was processed using the latest software versions as of mid-2016. The research is supported by UAS Pacific, the aim is to ultimately provide the industry with a better understanding of the data and aims to improve the overall quality of 3D modelling with the use of new exciting technologies and techniques that are available to the public today