355 research outputs found
Vision and Learning for Deliberative Monocular Cluttered Flight
Cameras provide a rich source of information while being passive, cheap and
lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work
we present the first implementation of receding horizon control, which is
widely used in ground vehicles, with monocular vision as the only sensing mode
for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a
number of contributions: novel coupling of perception and control via relevant
and diverse, multiple interpretations of the scene around the robot, leveraging
recent advances in machine learning to showcase anytime budgeted cost-sensitive
feature selection, and fast non-linear regression for monocular depth
prediction. We empirically demonstrate the efficacy of our novel pipeline via
real world experiments of more than 2 kms through dense trees with a quadrotor
built from off-the-shelf parts. Moreover our pipeline is designed to combine
information from other modalities like stereo and lidar as well if available
Vision-based localization methods under GPS-denied conditions
This paper reviews vision-based localization methods in GPS-denied
environments and classifies the mainstream methods into Relative Vision
Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss
the broad application of optical flow in feature extraction-based Visual
Odometry (VO) solutions and introduce advanced optical flow estimation methods.
For AVL, we review recent advances in Visual Simultaneous Localization and
Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman
Filter (EKF) based methods. We also introduce the application of offline map
registration and lane vision detection schemes to achieve Absolute Visual
Localization. This paper compares the performance and applications of
mainstream methods for visual localization and provides suggestions for future
studies.Comment: 32 pages, 15 figure
Self-supervised learning: When is fusion of the primary and secondary sensor cue useful?
Self-supervised learning (SSL) is a reliable learning mechanism in which a
robot enhances its perceptual capabilities. Typically, in SSL a trusted,
primary sensor cue provides supervised training data to a secondary sensor cue.
In this article, a theoretical analysis is performed on the fusion of the
primary and secondary cue in a minimal model of SSL. A proof is provided that
determines the specific conditions under which it is favorable to perform
fusion. In short, it is favorable when (i) the prior on the target value is
strong or (ii) the secondary cue is sufficiently accurate. The theoretical
findings are validated with computational experiments. Subsequently, a
real-world case study is performed to investigate if fusion in SSL is also
beneficial when assumptions of the minimal model are not met. In particular, a
flying robot learns to map pressure measurements to sonar height measurements
and then fuses the two, resulting in better height estimation. Fusion is also
beneficial in the opposite case, when pressure is the primary cue. The analysis
and results are encouraging to study SSL fusion also for other robots and
sensors
Visual Guidance for Unmanned Aerial Vehicles with Deep Learning
Unmanned Aerial Vehicles (UAVs) have been widely applied in the military and civilian domains. In recent years, the operation mode of UAVs is evolving from teleoperation to autonomous flight. In order to fulfill the goal of autonomous flight, a reliable guidance system is essential. Since the combination of Global Positioning System (GPS) and Inertial Navigation System (INS) systems cannot sustain autonomous flight in some situations where GPS can be degraded or unavailable, using computer vision as a primary method for UAV guidance has been widely explored. Moreover, GPS does not provide any information to the robot on the presence of obstacles.
Stereo cameras have complex architecture and need a minimum baseline to generate disparity map. By contrast, monocular cameras are simple and require less hardware resources. Benefiting from state-of-the-art Deep Learning (DL) techniques, especially Convolutional Neural Networks (CNNs), a monocular camera is sufficient to extrapolate mid-level visual representations such as depth maps and optical flow (OF) maps from the environment. Therefore, the objective of this thesis is to develop a real-time visual guidance method for UAVs in cluttered environments using a monocular camera and DL.
The three major tasks performed in this thesis are investigating the development of DL techniques and monocular depth estimation (MDE), developing real-time CNNs for MDE, and developing visual guidance methods on the basis of the developed MDE system. A comprehensive survey is conducted, which covers Structure from Motion (SfM)-based methods, traditional handcrafted feature-based methods, and state-of-the-art DL-based methods. More importantly, it also investigates the application of MDE in robotics. Based on the survey, two CNNs for MDE are developed. In addition to promising accuracy performance, these two CNNs run at high frame rates (126 fps and 90 fps respectively), on a single modest power Graphical Processing Unit (GPU).
As regards the third task, the visual guidance for UAVs is first developed on top of the designed MDE networks. To improve the robustness of UAV guidance, OF maps are integrated into the developed visual guidance method. A cross-attention module is applied to fuse the features learned from the depth maps and OF maps. The fused features are then passed through a deep reinforcement learning (DRL) network to generate the policy for guiding the flight of UAV. Additionally, a simulation framework is developed which integrates AirSim, Unreal Engine and PyTorch. The effectiveness of the developed visual guidance method is validated through extensive experiments in the simulation framework
An Autonomous Surface Vehicle for Long Term Operations
Environmental monitoring of marine environments presents several challenges:
the harshness of the environment, the often remote location, and most
importantly, the vast area it covers. Manual operations are time consuming,
often dangerous, and labor intensive. Operations from oceanographic vessels are
costly and limited to open seas and generally deeper bodies of water. In
addition, with lake, river, and ocean shoreline being a finite resource,
waterfront property presents an ever increasing valued commodity, requiring
exploration and continued monitoring of remote waterways. In order to
efficiently explore and monitor currently known marine environments as well as
reach and explore remote areas of interest, we present a design of an
autonomous surface vehicle (ASV) with the power to cover large areas, the
payload capacity to carry sufficient power and sensor equipment, and enough
fuel to remain on task for extended periods. An analysis of the design and a
discussion on lessons learned during deployments is presented in this paper.Comment: In proceedings of MTS/IEEE OCEANS, 2018, Charlesto
Vision-based Learning for Drones: A Survey
Drones as advanced cyber-physical systems are undergoing a transformative
shift with the advent of vision-based learning, a field that is rapidly gaining
prominence due to its profound impact on drone autonomy and functionality.
Different from existing task-specific surveys, this review offers a
comprehensive overview of vision-based learning in drones, emphasizing its
pivotal role in enhancing their operational capabilities under various
scenarios. We start by elucidating the fundamental principles of vision-based
learning, highlighting how it significantly improves drones' visual perception
and decision-making processes. We then categorize vision-based control methods
into indirect, semi-direct, and end-to-end approaches from the
perception-control perspective. We further explore various applications of
vision-based drones with learning capabilities, ranging from single-agent
systems to more complex multi-agent and heterogeneous system scenarios, and
underscore the challenges and innovations characterizing each area. Finally, we
explore open questions and potential solutions, paving the way for ongoing
research and development in this dynamic and rapidly evolving field. With
growing large language models (LLMs) and embodied intelligence, vision-based
learning for drones provides a promising but challenging road towards
artificial general intelligence (AGI) in 3D physical world
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