Goal detection using laser range finder for goalkeeper and striker from CAMBADA team

For a robot be autonomous and mobile, it requires being attached with a set of sensors that helps it to have a better perception of the surrounding world, to manage to localize itself and the surrounding objects. CAMBADA is the robotic soccer team of the IRIS research group, from IEETA, University of Aveiro, that competes in the Middle-Size League of RoboCup. In competition, in order to win, the main objective of the game it's to score more goals than the conceded, so not conceding goals, and score as much as possible it's desirable, thus, this thesis focus on adapt an agent with a better localization capacity in defensive and offensive moments. It was introduced a laser range finder to the CAMBADA robots, making them capable of detecting their own and the opponent goal, and to detect the opponents in specific game situations. With the new information and adapting the Goalie and Penalty behaviors, the CAMBADA goalkeeper is now able to detect and track its own goal and the CAMBADA striker has a better performance in a penalty situation. The developed work was incorporated within the competition software of the robots, which allows the presentation, in this thesis, of the experimental results obtained with physical robots on the laboratory field.

星载激光测距仪的高精度时间间隔测量单元

高精度时间间隔测量单元(TIU)是星载激光测距仪的关键部件。基于现场可编程门阵列(FPGA)研制出了满足星载要求的高精度、高集成度时间间隔测量单元。该单元采用数字计数法结合数字延迟线插入法的技术,在0.5～10 km的测量距离范围内,时间分辨率为500 ps。通过地面检测,在全程范围内保持了良好的线性度,标准偏差小于270 ps。该单元同时具备测量脉冲回波宽度的能力,可以获取目标的脉冲展宽信息。由于单元选用的元器件都具有航天产品性能,因此其设计和技术指标可满足星载激光测距仪的应用。

高重复率紧凑型微脉冲全固态激光器

本文设计并实现了一台高重复率，紧凑型微脉冲全固态激光器。YAG晶体， Nd:YAG晶体和Cr4+:YAG晶体键合为一个单块晶体作为谐振腔。优化计算了Cr4+:YAG晶体的初始透过率，耦合输出透射率和泵浦光斑大小。对激光器的性能进行了测试，结果表明该激光器适合于空间激光测距。

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.

Using image quality metrics to detect smoke-affected laser data

Field robots often rely on laser range finders (LRFs) to detect obstacles and navigate autonomously. Despite recent progress in sensing technology and perception algorithms, adverse environmental conditions, such as the presence of smoke, remain a challenging issue for these robots. In this paper, we investigate the possibility to improve laser-based perception applications by anticipating situations when laser data are affected by smoke, using supervised learning and state-of-the-art visual image quality analysis. We propose to train a k-nearest-neighbour (kNN) classifier to recognise situations where a laser scan is likely to be affected by smoke, based on visual data quality features. This method is evaluated experimentally using a mobile robot equipped with LRFs and a visual camera. The strengths and limitations of the technique are identified and discussed, and we show that the method is beneficial if conservative decisions are the most appropriate.

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.

Reliable automatic camera-laser calibration

Camera-laser calibration is necessary for many robotics and computer vision applications. However, existing calibration toolboxes still require laborious effort from the operator in order to achieve reliable and accurate results. This paper proposes algorithms that augment two existing trustful calibration methods with an automatic extraction of the calibration object from the sensor data. The result is a complete procedure that allows for automatic camera-laser calibration. The first stage of the procedure is automatic camera calibration which is useful in its own right for many applications. The chessboard extraction algorithm it provides is shown to outperform openly available techniques. The second stage completes the procedure by providing automatic camera-laser calibration. The procedure has been verified by extensive experimental tests with the proposed algorithms providing a major reduction in time required from an operator in comparison to manual methods.

Laser-camera data discrepancies and reliable perception in outdoor robotics

This work aims to promote integrity in autonomous perceptual systems, with a focus on outdoor unmanned ground vehicles equipped with a camera and a 2D laser range finder. A method to check for inconsistencies between the data provided by these two heterogeneous sensors is proposed and discussed. First, uncertainties in the estimated transformation between the laser and camera frames are evaluated and propagated up to the projection of the laser points onto the image. Then, for each pair of laser scan-camera image acquired, the information at corners of the laser scan is compared with the content of the image, resulting in a likelihood of correspondence. The result of this process is then used to validate segments of the laser scan that are found to be consistent with the image, while inconsistent segments are rejected. Experimental results illustrate how this technique can improve the reliability of perception in challenging environmental conditions, such as in the presence of airborne dust.

Description and instructions for use of Weldon range finder, revised March 1, 1906, revised February 1, 1909.

Seal of the Ordnance Department on t.p.

Distance measurement using the change in junction voltage across a laser diode due to the self-mixing effect

Conventional detection scheme for self-mixing sensors uses an integrated photodiode within the laser package to monitor the self mixing signal. This arrangement can be simplified by directly obtaining the self-mixing signals across the laser diode itself and omitting the photodiode. This work reports on a Vertical-Cavity Surface-Emitting Laser (VCSEL) based selfmixing sensor using the laser junction voltage to obtain the selfmixing signal. We show that the same information can be obtained with only minor changes to the extraction circuitry leading to potential cost saving with reductions in component costs and complexity and significant increase in bandwidth favoring high speed modulation. Experiments using both photo current and voltage detection were carried out and the results obtained show good agreement with the theory.

Towards Mapping of Rock Walls Using a UAV-Mounted 2D Laser Scanner in GPS Denied Environments

In geotechnical engineering, the stability of rock excavations and walls is estimated by using tools that include a map of the orientations of exposed rock faces. However, measuring these orientations by using conventional methods can be time consuming, sometimes dangerous, and is limited to regions of the exposed rock that are reachable by a human. This thesis introduces a 2D, simulated, quadcopter-based rock wall mapping algorithm for GPS denied environments such as underground mines or near high walls on surface. The proposed algorithm employs techniques from the field of robotics known as simultaneous localization and mapping (SLAM) and is a step towards 3D rock wall mapping. Not only are quadcopters agile, but they can hover. This is very useful for confined spaces such as underground or near rock walls. The quadcopter requires sensors to enable self localization and mapping in dark, confined and GPS denied environments. However, these sensors are limited by the quadcopter payload and power restrictions. Because of these restrictions, a light weight 2D laser scanner is proposed. As a first step towards a 3D mapping algorithm, this thesis proposes a simplified scenario in which a simulated 1D laser range finder and 2D IMU are mounted on a quadcopter that is moving on a plane. Because the 1D laser does not provide enough information to map the 2D world from a single measurement, many measurements are combined over the trajectory of the quadcopter. Least Squares Optimization (LSO) is used to optimize the estimated trajectory and rock face for all data collected over the length of a light. Simulation results show that the mapping algorithm developed is a good first step. It shows that by combining measurements over a trajectory, the scanned rock face can be estimated using a lower-dimensional range sensor. A swathing manoeuvre is introduced as a way to promote loop closures within a short time period, thus reducing accumulated error. Some suggestions on how to improve the algorithm are also provided.