A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

Predictive Duty Cycle Adaptation for Wireless Camera Networks

Wireless sensor networks (WSN) typically employ dynamic duty cycle schemes to efficiently handle different patterns of communication traffic in the network. However, existing duty cycling approaches are not suitable for event-driven WSN, in particular, camera-based networks designed to track humans and objects. A characteristic feature of such networks is the spatially-correlated bursty traffic that occurs in the vicinity of potentially highly mobile objects. In this paper, we propose a concept of indirect sensing in the MAC layer of a wireless camera network and an active duty cycle adaptation scheme based on Kalman filter that continuously predicts and updates the location of the object that triggers bursty communication traffic in the network. This prediction allows the camera nodes to alter their communication protocol parameters prior to the actual increase in the communication traffic. Our simulations demonstrate that our active adaptation strategy outperforms TMAC not only in terms of energy efficiency and communication latency, but also in terms of TIBPEA, a QoS metric for event-driven WSN

Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability

Realtime State Estimation with Tactile and Visual sensing. Application to Planar Manipulation

Accurate and robust object state estimation enables successful object manipulation. Visual sensing is widely used to estimate object poses. However, in a cluttered scene or in a tight workspace, the robot's end-effector often occludes the object from the visual sensor. The robot then loses visual feedback and must fall back on open-loop execution. In this paper, we integrate both tactile and visual input using a framework for solving the SLAM problem, incremental smoothing and mapping (iSAM), to provide a fast and flexible solution. Visual sensing provides global pose information but is noisy in general, whereas contact sensing is local, but its measurements are more accurate relative to the end-effector. By combining them, we aim to exploit their advantages and overcome their limitations. We explore the technique in the context of a pusher-slider system. We adapt iSAM's measurement cost and motion cost to the pushing scenario, and use an instrumented setup to evaluate the estimation quality with different object shapes, on different surface materials, and under different contact modes

Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios

Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. These cameras do not suffer from motion blur and have a very high dynamic range, which enables them to provide reliable visual information during high speed motions or in scenes characterized by high dynamic range. However, event cameras output only little information when the amount of motion is limited, such as in the case of almost still motion. Conversely, standard cameras provide instant and rich information about the environment most of the time (in low-speed and good lighting scenarios), but they fail severely in case of fast motions, or difficult lighting such as high dynamic range or low light scenes. In this paper, we present the first state estimation pipeline that leverages the complementary advantages of these two sensors by fusing in a tightly-coupled manner events, standard frames, and inertial measurements. We show on the publicly available Event Camera Dataset that our hybrid pipeline leads to an accuracy improvement of 130% over event-only pipelines, and 85% over standard-frames-only visual-inertial systems, while still being computationally tractable. Furthermore, we use our pipeline to demonstrate - to the best of our knowledge - the first autonomous quadrotor flight using an event camera for state estimation, unlocking flight scenarios that were not reachable with traditional visual-inertial odometry, such as low-light environments and high-dynamic range scenes.Comment: 8 pages, 9 figures, 2 table

A surgical system for automatic registration, stiffness mapping and dynamic image overlay

In this paper we develop a surgical system using the da Vinci research kit (dVRK) that is capable of autonomously searching for tumors and dynamically displaying the tumor location using augmented reality. Such a system has the potential to quickly reveal the location and shape of tumors and visually overlay that information to reduce the cognitive overload of the surgeon. We believe that our approach is one of the first to incorporate state-of-the-art methods in registration, force sensing and tumor localization into a unified surgical system. First, the preoperative model is registered to the intra-operative scene using a Bingham distribution-based filtering approach. An active level set estimation is then used to find the location and the shape of the tumors. We use a recently developed miniature force sensor to perform the palpation. The estimated stiffness map is then dynamically overlaid onto the registered preoperative model of the organ. We demonstrate the efficacy of our system by performing experiments on phantom prostate models with embedded stiff inclusions.Comment: International Symposium on Medical Robotics (ISMR 2018

People tracking by cooperative fusion of RADAR and camera sensors

Accurate 3D tracking of objects from monocular camera poses challenges due to the loss of depth during projection. Although ranging by RADAR has proven effective in highway environments, people tracking remains beyond the capability of single sensor systems. In this paper, we propose a cooperative RADAR-camera fusion method for people tracking on the ground plane. Using average person height, joint detection likelihood is calculated by back-projecting detections from the camera onto the RADAR Range-Azimuth data. Peaks in the joint likelihood, representing candidate targets, are fed into a Particle Filter tracker. Depending on the association outcome, particles are updated using the associated detections (Tracking by Detection), or by sampling the raw likelihood itself (Tracking Before Detection). Utilizing the raw likelihood data has the advantage that lost targets are continuously tracked even if the camera or RADAR signal is below the detection threshold. We show that in single target, uncluttered environments, the proposed method entirely outperforms camera-only tracking. Experiments in a real-world urban environment also confirm that the cooperative fusion tracker produces significantly better estimates, even in difficult and ambiguous situations