All-environment visual place recognition with SMART

This paper presents Sequence Matching Across Route Traversals (SMART); a generally applicable sequence-based place recognition algorithm. SMART provides invariance to changes in illumination and vehicle speed while also providing moderate pose invariance and robustness to environmental aliasing. We evaluate SMART on vehicles travelling at highly variable speeds in two challenging environments; firstly, on an all-terrain vehicle in an off-road, forest track and secondly, using a passenger car traversing an urban environment across day and night. We provide comparative results to the current state-of-the-art SeqSLAM algorithm and investigate the effects of altering SMART’s image matching parameters. Additionally, we conduct an extensive study of the relationship between image sequence length and SMART’s matching performance. Our results show viable place recognition performance in both environments with short 10-metre sequences, and up to 96% recall at 100% precision across extreme day-night cycles when longer image sequences are used.

Empirical modelling of rolling shutter effect

We propose and evaluate a novel methodology to identify the rolling shutter parameters of a real camera. We also present a model for the geometric distortion introduced when a moving camera with a rolling shutter views a scene. Unlike previous work this model allows for arbitrary camera motion, including accelerations, is exact rather than a linearization and allows for arbitrary camera projection models, for example fisheye or panoramic. We show the significance of the errors introduced by a rolling shutter for typical robot vision problems such as structure from motion, visual odometry and pose estimation.

Simultaneous localization and planning on multiple map hypotheses

This paper presents a novel method to rank map hypotheses by the quality of localization they afford. The highest ranked hypothesis at any moment becomes the active representation that is used to guide the robot to its goal location. A single static representation is insufficient for navigation in dynamic environments where paths can be blocked periodically, a common scenario which poses significant challenges for typical planners. In our approach we simultaneously rank multiple map hypotheses by the influence that localization in each of them has on locally accurate odometry. This is done online for the current locally accurate window by formulating a factor graph of odometry relaxed by localization constraints. Comparison of the resulting perturbed odometry of each hypothesis with the original odometry yields a score that can be used to rank map hypotheses by their utility. We deploy the proposed approach on a real robot navigating a structurally noisy office environment. The configuration of the environment is physically altered outside the robots sensory horizon during navigation tasks to demonstrate the proposed approach of hypothesis selection.

Shared autonomy for close-quarters navigation and control of a VTOL platform

This thesis presents an approach for a vertical infrastructure inspection using a vertical take-off and landing (VTOL) unmanned aerial vehicle and shared autonomy. Inspecting vertical structure such as light and power distribution poles is a difficult task. There are challenges involved with developing such an inspection system, such as flying in close proximity to a target while maintaining a fixed stand-off distance from it. The contributions of this thesis fall into three main areas. Firstly, an approach to vehicle dynamic modeling is evaluated in simulation and experiments. Secondly, EKF-based state estimators are demonstrated, as well as estimator-free approaches such as image based visual servoing (IBVS) validated with motion capture ground truth data. Thirdly, an integrated pole inspection system comprising a VTOL platform with human-in-the-loop control, (shared autonomy) is demonstrated. These contributions are comprehensively explained through a series of published papers.

Capturing natural-colour 3D models of insects for species discovery and diagnostics

Collections of biological specimens are fundamental to scientific understanding and characterization of natural diversity - past, present and future. This paper presents a system for liberating useful information from physical collections by bringing specimens into the digital domain so they can be more readily shared, analyzed, annotated and compared. It focuses on insects and is strongly motivated by the desire to accelerate and augment current practices in insect taxonomy which predominantly use text, 2D diagrams and images to describe and characterize species. While these traditional kinds of descriptions are informative and useful, they cannot cover insect specimens "from all angles" and precious specimens are still exchanged between researchers and collections for this reason. Furthermore, insects can be complex in structure and pose many challenges to computer vision systems. We present a new prototype for a practical, cost-effective system of off-the-shelf components to acquire natural-colour 3D models of insects from around 3 mm to 30 mm in length. ("Natural-colour" is used to contrast with "false-colour", i.e., colour generated from, or applied to, gray-scale data post-acquisition.) Colour images are captured from different angles and focal depths using a digital single lens reflex (DSLR) camera rig and two-axis turntable. These 2D images are processed into 3D reconstructions using software based on a visual hull algorithm. The resulting models are compact (around 10 megabytes), afford excellent optical resolution, and can be readily embedded into documents and web pages, as well as viewed on mobile devices. The system is portable, safe, relatively affordable, and complements the sort of volumetric data that can be acquired by computed tomography. This system provides a new way to augment the description and documentation of insect species holotypes, reducing the need to handle or ship specimens. It opens up new opportunities to collect data for research, education, art, entertainment, biodiversity assessment and biosecurity control. © 2014 Nguyen et al.

Simultaneous underwater visibility assessment, enhancement and improved stereo

Vision-based underwater navigation and obstacle avoidance demands robust computer vision algorithms, particularly for operation in turbid water with reduced visibility. This paper describes a novel method for the simultaneous underwater image quality assessment, visibility enhancement and disparity computation to increase stereo range resolution under dynamic, natural lighting and turbid conditions. The technique estimates the visibility properties from a sparse 3D map of the original degraded image using a physical underwater light attenuation model. Firstly, an iterated distance-adaptive image contrast enhancement enables a dense disparity computation and visibility estimation. Secondly, using a light attenuation model for ocean water, a color corrected stereo underwater image is obtained along with a visibility distance estimate. Experimental results in shallow, naturally lit, high-turbidity coastal environments show the proposed technique improves range estimation over the original images as well as image quality and color for habitat classification. Furthermore, the recursiveness and robustness of the technique allows implementation onboard an Autonomous Underwater Vehicle for improving navigation and obstacle avoidance performance.

Evaluation of internal navigation sensor suites for underground mining vehicle navigation

This paper describes a series of trials that were done at an underground mine in New South Wales, Australia. Experimental results are presented from the data obtained during the field trials and suitable sensor suites for an autonomous mining vehicle navigation system are evaluated.

Modeling and control of a robotic power line inspection vehicle

Power line inspection is a vital function for electricity supply companies but it involves labor-intensive and expensive procedures which are tedious and error-prone for humans to perform. A possible solution is to use an unmanned aerial vehicle (UAV) equipped with video surveillance equipment to perform the inspection. This paper considers how a small, electrically driven rotorcraft conceived for this application could be controlled by visually tracking the overhead supply lines. A dynamic model for a ducted-fan rotorcraft is presented and used to control the action of an Air Vehicle Simulator (AVS), consisting of a cable-array robot. Results show how visual data can be used to determine, and hence regulate in closed loop, the simulated vehicle’s position relative to the overhead lines.

Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach

Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80% and improve precision to 27% by using DCNNs as the core approach.

基于现场总线的工业机器人联网

本文根据国内工业机器人技术开发和应用现状及其技术发展趋势 ,进行了基于现场总线的工业机器人联网技术的研究和开发 ,并将机器人作为生产线底层设备 ,实现了工业机器人网络的互联 .本文介绍了这个系统的硬件结构、上位监控机软件实现、控制器软件实现以及系统完成的功能 .

基于QNX实时系统的行为辅助机器人控制系统

论文设计研制了行为辅助机器人验证平台,以此平台为基础可以针对行为辅助机器人进行力控制研究。针对机器人力控制的实际要求,设计了行为辅助机器人柔性关节结构,辨识了系统参数。基于QNX实时操作系统设计了控制系统软件。机器人控制系统软件主要包括传感器数据采集和控制算法两部分,可以满足柔性关节控制需求。

智能机器人多传感器实验平台的图像伺服系统

结合智能机器人多传感器实验平台的研制工作 ,系统介绍了其图像伺服子系统的工作原理、系统构成以及实现方案 ,涉及图像处理、机器视觉和机器人跟踪控制等相关内容 ,并讨论了物体自动识别和抓取过程中需要注意的一些问题 .

机器人多传感器实验平台中的图像传感器在线标定研究

本文结合智能机器人多传感器实验平台的研制工作,系统地介绍了图像传感器在线标定的原理和方法,并对标定系统的构成做了概述,同时结合实验对相关问题进行进一步的探讨。

网络遥操作机器人采样系统模型与控制研究

网络遥操作机器人系统是网络技术与机器人技术相结合的产物。它延伸了操作者的感知和操作能力，使操作者可以置身于安全的环境中而完成危险环境中的作业任务；提高了机器人对工作环境的适应能力，辅之以操作者的决策，机器人可以工作于非结构化的工作环境中。网络遥操作机器人技术作为机器人学的一个重要分支，近十几年来受到许多研究机构和研究人员的关注和重视。 本文针对网络遥操作机器人采样控制结构，通过控制策略解决非结构环境下网络遥操作机器人的实时控制问题。为此，首先研究了网络遥操作机器人采样系统建模问题。目前，关于网络遥操作机器人采样系统模型大多是针对单采样周期的，主从端不同采样周期的统一模型目前还没有相关报道。由于操作者是网络遥操作机器人采样系统的组成部分，因此若建立网络遥操作机器人采样系统模型首先要建立操作者模型。然而由于操作者建模问题比较复杂，目前在遥操作系统建模时，一般都避开了操作者建模问题。本文在分析了现有的遥操作控制方式和遥操作系统模型的基础上，主要针对网络遥操作机器人采样系统模型和控制问题进行研究。 操作者模型研究方面，主要以操作者用小臂操作具有力反馈功能的操纵杆为例，研究操作者操作操纵杆过程的动态模型建模方法。首先对人体骨骼肌肌肉力学模型中不可测量，即肌肉激活度，通过实验进行研究，得出在操作者保持紧张程度不变情况下“肌肉激活度”与肌肉收缩长度的关系。在此基础上，考虑手臂的动力学特性、操纵杆的动力学特性，建立了肌肉力驱动的手臂—操纵杆系统动力学模型。在操作者模型的基础上，设计动态补偿器，补偿操作者操作操纵杆的动态过程，解决由于肌肉动态特性被污染所造成的操作者所想与所做不一致的问题，克服操作者操作时延，提高网络遥操作机器人系统的性能。 遥操作机器人采样系统模型研究方面，首先针对主从端不同采样周期的网络遥操作机器人采样控制结构，通过引入双端口RAM的方法，实现网络遥操作机器人系统主从端的采样同步；在网络遥操作机器人采样同步控制结构模型的基础上，建立从端离散状态空间表达式，利用提升技术对从端离散状态空间表达式按遥操作周期提升，利用采样系统理论得到主从端统一的网络遥操作机器人采样系统模型；最后对从端系统提升前后的稳定性、可控性、可观测性进行分析，得出从端系统提升前后稳定性、可控性、可观测性不变的结论。 遥操作控制策略研究方面，提出基于时延预测的采样切换控制方法。首先对互联网节点间的网络时延进行测试分析，得出任意两个网络节点间时延分布规律，即任意时间段内网络时延的概率密度都可以用平移Gamma分布曲线描述。采用拟合样本概率密度曲线的方法，对平移Gamma参数进行预估，得出平移Gamma分布的种类，进而根据平移Gamma分布的种类，确定出网络时延的均值，最后确定出期望的采样周期；为了实现任意采样周期下切换系统的稳定控制，对采样切换系统的稳定性进行了研究，得到如下结论，即如果从端系统一致渐进稳定，则对从端实行任意采样切换控制时网络遥操作机器人采样系统是稳定的。 为了对所研究内容进行实验验证，以移动机器人为被控对象，搭建了一个具有力反馈控制和局部自主功能的网络遥操作机器人采样系统实验平台。用人工势场法建立了虚拟力模型并给出了虚拟力在力反馈操纵杆上的实现方法；以移动机器人自主避障为例，给出了从端自主的模糊控制设计方法和实验系统遥操作软件设计方法。 实验结果证明了所提出的模型和控制方法是有效的、可行的，对于建立性能良好的网络遥操作机器人系统具有现实意义。本文所研究的许多结论，对于一般网络遥操作机器人系统的理论研究和实际应用也具有一定的参考价值。