2,558 research outputs found
Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots
Safety is paramount for mobile robotic platforms such as self-driving cars
and unmanned aerial vehicles. This work is devoted to a task that is
indispensable for safety yet was largely overlooked in the past -- detecting
obstacles that are of very thin structures, such as wires, cables and tree
branches. This is a challenging problem, as thin objects can be problematic for
active sensors such as lidar and sonar and even for stereo cameras. In this
work, we propose to use video sequences for thin obstacle detection. We
represent obstacles with edges in the video frames, and reconstruct them in 3D
using efficient edge-based visual odometry techniques. We provide both a
monocular camera solution and a stereo camera solution. The former incorporates
Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter
enjoys a novel, purely vision-based solution. Experiments demonstrated that the
proposed methods are fast and able to detect thin obstacles robustly and
accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio
Depth Fields: Extending Light Field Techniques to Time-of-Flight Imaging
A variety of techniques such as light field, structured illumination, and
time-of-flight (TOF) are commonly used for depth acquisition in consumer
imaging, robotics and many other applications. Unfortunately, each technique
suffers from its individual limitations preventing robust depth sensing. In
this paper, we explore the strengths and weaknesses of combining light field
and time-of-flight imaging, particularly the feasibility of an on-chip
implementation as a single hybrid depth sensor. We refer to this combination as
depth field imaging. Depth fields combine light field advantages such as
synthetic aperture refocusing with TOF imaging advantages such as high depth
resolution and coded signal processing to resolve multipath interference. We
show applications including synthesizing virtual apertures for TOF imaging,
improved depth mapping through partial and scattering occluders, and single
frequency TOF phase unwrapping. Utilizing space, angle, and temporal coding,
depth fields can improve depth sensing in the wild and generate new insights
into the dimensions of light's plenoptic function.Comment: 9 pages, 8 figures, Accepted to 3DV 201
Single-pixel, single-photon three-dimensional imaging
The 3D recovery of a scene is a crucial task with many real-life applications such as self-driving vehicles, X-ray tomography and virtual reality. The recent development of time-resolving detectors sensible to single photons allowed the recovery of the 3D information at high frame rate with unprecedented capabilities. Combined with a timing system, single-photon sensitive detectors
allow the 3D image recovery by measuring the Time-of-Flight (ToF) of the photons scattered back by the scene with a millimetre depth resolution.
Current ToF 3D imaging techniques rely on scanning detection systems or multi-pixel sensor.
Here, we discuss an approach to simplify the hardware complexity of the current 3D imaging ToF techniques using a single-pixel, single-photon sensitive detector and computational imaging algorithms. The 3D imaging approaches discussed in this thesis do not require mechanical moving
parts as in standard Lidar systems. The single-pixel detector allows to reduce the pixel complexity to a single unit and offers several advantages in terms of size, flexibility, wavelength range and cost. The experimental results demonstrate the 3D image recovery of hidden scenes with a subsecond
acquisition time, allowing also non-line-of-sight scenes 3D recovery in real-time. We also introduce the concept of intelligent Lidar, a 3D imaging paradigm based uniquely on the temporal trace of the return photons and a data-driven 3D retrieval algorithm
Cross Hallway Detection and Indoor Localization Using Flash Laser Detection and Ranging
A flash LADAR is investigated as a source of navigation information to support cross-hallway detection and relative localization. To accomplish this, a dynamic, flexible simulation was developed that simulated the LADAR and the noise of a LADAR system. Using simulated LADAR data, algorithms were developed that were shown to be effective at detecting cross hallways in simulated ideal environments and in simulated environments with noise. Relative position was determined in the same situations. A SwissRanger SR4000 flash LADAR was then used to collect real data and to verify algorithm performance in real environments. Hallway detection was shown to be possible in all real data sets, and the relative position-finding algorithm was shown to be accurate when compared to the absolute accuracy of the LADAR. Thus, flash LADAR is concluded to be an effective source for indoor navigation information
Pix2HDR -- A pixel-wise acquisition and deep learning-based synthesis approach for high-speed HDR videos
Accurately capturing dynamic scenes with wide-ranging motion and light
intensity is crucial for many vision applications. However, acquiring
high-speed high dynamic range (HDR) video is challenging because the camera's
frame rate restricts its dynamic range. Existing methods sacrifice speed to
acquire multi-exposure frames. Yet, misaligned motion in these frames can still
pose complications for HDR fusion algorithms, resulting in artifacts. Instead
of frame-based exposures, we sample the videos using individual pixels at
varying exposures and phase offsets. Implemented on a pixel-wise programmable
image sensor, our sampling pattern simultaneously captures fast motion at a
high dynamic range. We then transform pixel-wise outputs into an HDR video
using end-to-end learned weights from deep neural networks, achieving high
spatiotemporal resolution with minimized motion blurring. We demonstrate
aliasing-free HDR video acquisition at 1000 FPS, resolving fast motion under
low-light conditions and against bright backgrounds - both challenging
conditions for conventional cameras. By combining the versatility of pixel-wise
sampling patterns with the strength of deep neural networks at decoding complex
scenes, our method greatly enhances the vision system's adaptability and
performance in dynamic conditions.Comment: 14 pages, 14 figure
Towards gestural understanding for intelligent robots
Fritsch JN. Towards gestural understanding for intelligent robots. Bielefeld: Universität Bielefeld; 2012.A strong driving force of scientific progress in the technical sciences is the quest for systems that assist humans in their daily life and make their life easier and more enjoyable. Nowadays smartphones are probably the most typical instances of such systems. Another class of systems that is getting increasing attention are intelligent robots. Instead of offering a smartphone touch screen to select actions, these systems are intended to offer a more natural human-machine interface to their users. Out of the large range of actions performed by humans, gestures performed with the hands play a very important role especially when humans interact with their direct surrounding like, e.g., pointing to an object or manipulating it. Consequently, a robot has to understand such gestures to offer an intuitive interface. Gestural understanding is, therefore, a key capability on the way to intelligent robots.
This book deals with vision-based approaches for gestural understanding. Over the past two decades, this has been an intensive field of research which has resulted in a variety of algorithms to analyze human hand motions. Following a categorization of different gesture types and a review of other sensing techniques, the design of vision systems that achieve hand gesture understanding for intelligent robots is analyzed. For each of the individual algorithmic steps – hand detection, hand tracking, and trajectory-based gesture recognition – a separate Chapter introduces common techniques and algorithms and provides example methods. The resulting recognition algorithms are considering gestures in isolation and are often not sufficient for interacting with a robot who can only understand such gestures when incorporating the context like, e.g., what object was pointed at or manipulated.
Going beyond a purely trajectory-based gesture recognition by incorporating context is an important prerequisite to achieve gesture understanding and is addressed explicitly in a separate Chapter of this book. Two types of context, user-provided context and situational context, are reviewed and existing approaches to incorporate context for gestural understanding are reviewed. Example approaches for both context types provide a deeper algorithmic insight into this field of research. An overview of recent robots capable of gesture recognition and understanding summarizes the currently realized human-robot interaction quality.
The approaches for gesture understanding covered in this book are manually designed while humans learn to recognize gestures automatically during growing up. Promising research targeted at analyzing developmental learning in children in order to mimic this capability in technical systems is highlighted in the last Chapter completing this book as this research direction may be highly influential for creating future gesture understanding systems
Motion tracking problems in Internet of Things (IoT) and wireless networking
The dissertation focuses on inferring various motion patterns of internet-of-things (IoT) devices, by leveraging inertial sensors embedded in these objects, as well as wireless signals emitted (or reflected) from them. For instance, we use a combination of GPS signals and inertial sensors on drones to precisely track its 3D orientation over time, ultimately improving safety against failures and crashes. In another application in sports analytics, we embed sensors and radios inside baseballs and cricket balls and compute their 3D trajectory and spin patterns, even when they move at extremely high speeds. In a third application for wireless networks, we explore the possibility of physically moving wireless infrastructure like Access Points and basestations on robots and drones for enhancing the network performance. While these are diverse applications in drones, sports analytics, and wireless networks, the common theme underlying the research is in the development of the core motion-related building blocks. Specifically, we emphasize the philosophy of "fusion of multi modal sensor data with application specific model” as the design principle for building the next generation of diverse IoT applications. To this end, we draw on theoretical techniques in wireless communication, signal processing, and statistics, but translate them to completely functional systems on real-world platforms
- …