Laser-radar data fusion with Gaussian Process Implicit Surfaces

This work considers the problem of building high-fidelity 3D representations of the environment from sensor data acquired by mobile robots. Multi-sensor data fusion allows for more complete and accurate representations, and for more reliable perception, especially when different sensing modalities are used. In this paper, we propose a thorough experimental analysis of the performance of 3D surface reconstruction from laser and mm-wave radar data using Gaussian Process Implicit Surfaces (GPIS), in a realistic field robotics scenario. We first analyse the performance of GPIS using raw laser data alone and raw radar data alone, respectively, with different choices of covariance matrices and different resolutions of the input data. We then evaluate and compare the performance of two different GPIS fusion approaches. The first, state-of-the-art approach directly fuses raw data from laser and radar. The alternative approach proposed in this paper first computes an initial estimate of the surface from each single source of data, and then fuses these two estimates. We show that this method outperforms the state of the art, especially in situations where the sensors react differently to the targets they perceive.

Theory of radar imaging using time-frequency distribution

The concept of radar was developed for the estimation of the distance (range) and velocity of a target from a receiver. The distance measurement is obtained by measuring the time taken for the transmitted signal to propagate to the target and return to the receiver. The target's velocity is determined by measuring the Doppler induced frequency shift of the returned signal caused by the rate of change of the time- delay from the target. As researchers further developed conventional radar systems it become apparent that additional information was contained in the backscattered signal and that this information could in fact be used to describe the shape of the target itself. It is due to the fact that a target can be considered to be a collection of individual point scatterers, each of which has its own velocity and time- delay. DelayDoppler parameter estimation of each of these point scatterers thus corresponds to a mapping of the target's range and cross range, thus producing an image of the target. Much research has been done in this area since the early radar imaging work of the 1960s. At present there are two main categories into which radar imaging falls. The first of these is related to the case where the backscattered signal is considered to be deterministic. The second is related to the case where the backscattered signal is of a stochastic nature. In both cases the information which describes the target's scattering function is extracted by the use of the ambiguity function, a function which correlates the backscattered signal in time and frequency with the transmitted signal. In practical situations, it is often necessary to have the transmitter and the receiver of the radar system sited at different locations. The problem in these situations is 'that a reference signal must then be present in order to calculate the ambiguity function. This causes an additional problem in that detailed phase information about the transmitted signal is then required at the receiver. It is this latter problem which has led to the investigation of radar imaging using time- frequency distributions. As will be shown in this thesis, the phase information about the transmitted signal can be extracted from the backscattered signal using time- frequency distributions. The principle aim of this thesis was in the development, and subsequent discussion into the theory of radar imaging, using time- frequency distributions. Consideration is first given to the case where the target is diffuse, ie. where the backscattered signal has temporal stationarity and a spatially white power spectral density. The complementary situation is also investigated, ie. where the target is no longer diffuse, but some degree of correlation exists between the time- frequency points. Computer simulations are presented to demonstrate the concepts and theories developed in the thesis. For the proposed radar system to be practically realisable, both the time- frequency distributions and the associated algorithms developed must be able to be implemented in a timely manner. For this reason an optical architecture is proposed. This architecture is specifically designed to obtain the required time and frequency resolution when using laser radar imaging. The complex light amplitude distributions produced by this architecture have been computer simulated using an optical compiler.

Mid-infrared passively switched pulsed dual wavelength Ho3+ -doped fluoride fiber laser at 3 μm and 2 μm

Cascade transitions of rare earth ions involved in infrared host fiber provide the potential to generate dual or multiple wavelength lasing at mid-infrared region. In addition, the fast development of saturable absorber (SA) towards the long wavelengths motivates the realization of passively switched mid-infrared pulsed lasers. In this work, by combing the above two techniques, a new phenomenon of passively Q-switched ~3 μm and gain-switched ~2 μm pulses in a shared cavity was demonstrated with a Ho3+-doped fluoride fiber and a specifically designed semiconductor saturable absorber (SESAM) as the SA. The repetition rate of ~2 μm pulses can be tuned between half and same as that of ~3 μm pulses by changing the pump power. The proposed method here will add new capabilities and more flexibility for generating mid-infrared multiple wavelength pulses simultaneously that has important potential applications for laser surgery, material processing, laser radar, and free-space communications, and other areas.

Lost in translation (and rotation) : rapid extrinsic calibration for 2D and 3D LIDARs

This paper describes a novel method for determining the extrinsic calibration parameters between 2D and 3D LIDAR sensors with respect to a vehicle base frame. To recover the calibration parameters we attempt to optimize the quality of a 3D point cloud produced by the vehicle as it traverses an unknown, unmodified environment. The point cloud quality metric is derived from Rényi Quadratic Entropy and quantifies the compactness of the point distribution using only a single tuning parameter. We also present a fast approximate method to reduce the computational requirements of the entropy evaluation, allowing unsupervised calibration in vast environments with millions of points. The algorithm is analyzed using real world data gathered in many locations, showing robust calibration performance and substantial speed improvements from the approximations.

Robust multiple-sensing-modality data fusion using Gaussian Process Implicit Surfaces

The ability to build high-fidelity 3D representations of the environment from sensor data is critical for autonomous robots. Multi-sensor data fusion allows for more complete and accurate representations. Furthermore, using distinct sensing modalities (i.e. sensors using a different physical process and/or operating at different electromagnetic frequencies) usually leads to more reliable perception, especially in challenging environments, as modalities may complement each other. However, they may react differently to certain materials or environmental conditions, leading to catastrophic fusion. In this paper, we propose a new method to reliably fuse data from multiple sensing modalities, including in situations where they detect different targets. We first compute distinct continuous surface representations for each sensing modality, with uncertainty, using Gaussian Process Implicit Surfaces (GPIS). Second, we perform a local consistency test between these representations, to separate consistent data (i.e. data corresponding to the detection of the same target by the sensors) from inconsistent data. The consistent data can then be fused together, using another GPIS process, and the rest of the data can be combined as appropriate. The approach is first validated using synthetic data. We then demonstrate its benefit using a mobile robot, equipped with a laser scanner and a radar, which operates in an outdoor environment in the presence of large clouds of airborne dust and smoke.

基于电光材料的光学相控阵技术研究进展

综述了基于电光材料的光学相控阵(OPA)的研究进展。介绍了光学相控阵技术的基本原理以及不同电光材料(铌酸锂电光晶体,AlGaAs光波导,液晶和掺镧锆钛酸铅(PLZT)电光陶瓷)光学相控阵技术的基本构想和涉及的关键技术;着重介绍近年来基于PLZT电光陶瓷材料光学相控阵技术的发展情况以及在这方面的最新研究成果,包括单级相控阵、级联相控阵、不同电极结构(表面电极和上下电极结构)相控阵技术等;最后简要介绍了光学相控阵技术在激光雷达等军事领域中的应用。

Intelligent control of avalanched photodiode to get optimal state

To improve the sensitivity of our laser radar system, we provided a set of control method for APDs (Avalanched Photodiodes) based on single-chip computer together with the circuits dealing with noise and temperature. It adjusts the voltages intelligently and maintains the APD's optimal working status.

Laser-to-radar sensing redundancy for resilient perception in adverse environmental conditions

This paper presents an approach to promote the integrity of perception systems for outdoor unmanned ground vehicles (UGV) operating in challenging environmental conditions (presence of dust or smoke). The proposed technique automatically evaluates the consistency of the data provided by two sensing modalities: a 2D laser range finder and a millimetre-wave radar, allowing for perceptual failure mitigation. Experimental results, obtained with a UGV operating in rural environments, and an error analysis validate the approach.

Augmenting traversability maps with ultra-wideband radar to enhance obstacle detection in vegetated environments

Operating in vegetated environments is a major challenge for autonomous robots. Obstacle detection based only on geometric features causes the robot to consider foliage, for example, small grass tussocks that could be easily driven through, as obstacles. Classifying vegetation does not solve this problem since there might be an obstacle hidden behind the vegetation. In addition, dense vegetation typically needs to be considered as an obstacle. This paper addresses this problem by augmenting probabilistic traversability map constructed from laser data with ultra-wideband radar measurements. An adaptive detection threshold and a probabilistic sensor model are developed to convert the radar data to occupancy probabilities. The resulting map captures the fine resolution of the laser map but clears areas from the traversability map that are induced by obstacle-free foliage. Experimental results validate that this method is able to improve the accuracy of traversability maps in vegetated environments.

A laser driven pulsed X-ray backscatter technique for enhanced penetrative imaging

X-ray backscatter imaging can be used for a wide range of imaging applications, in particular for industrial inspection and portal security. Currently, the application of this imaging technique to the detection of landmines is limited due to the surrounding sand or soil strongly attenuating the 10s to 100s of keV X-rays required for backscatter imaging. Here, we introduce a new approach involving a 140 MeV short-pulse (< 100 fs) electron beam generated by laser wakefield acceleration to probe the sample, which produces Bremsstrahlung X-rays within the sample enabling greater depths to be imaged. A variety of detector and scintillator configurations are examined, with the best time response seen from an absorptive coated BaF₂ scintillator with a bandpass filter to remove the slow scintillation emission components. An X-ray backscatter image of an array of different density and atomic number items is demonstrated. The use of a compact laser wakefield accelerator to generate the electron source, combined with the rapid development of more compact, efficient and higher repetition rate high power laser systems will make this system feasible for applications in the field.

Detecção de áreas de oclusão para geração de ortoimagem verdadeira utilizando dados LASER

Pós-graduação em Ciências Cartográficas - FCT