Non-Metrical Navigation Through Visual Path Control

We describe a new method for wide-area, non-metrical robot navigationwhich enables useful, purposeful motion indoors. Our method has twophases: a training phase, in which a human user directs a wheeledrobot with an attached camera through an environment while occasionallysupplying textual place names; and a navigation phase in which theuser specifies goal place names (again as text), and the robot issueslow-level motion control in order to move to the specified place. We show thatdifferences in the visual-field locations and scales of features matched acrosstraining and navigation can be used to construct a simple and robust controlrule that guides the robot onto and along the training motion path.Our method uses an omnidirectional camera, requires approximateintrinsic and extrinsic camera calibration, and is capable of effective motioncontrol within an extended, minimally-prepared building environment floorplan.We give results for deployment within a single building floor with 7 rooms, 6corridor segments, and 15 distinct place names

Bose-Einstein condensation in complex networks

The evolution of many complex systems, including the world wide web, business and citation networks is encoded in the dynamic web describing the interactions between the system's constituents. Despite their irreversible and non-equilibrium nature these networks follow Bose statistics and can undergo Bose-Einstein condensation. Addressing the dynamical properties of these non-equilibrium systems within the framework of equilibrium quantum gases predicts that the 'first-mover-advantage', 'fit-get-rich' and 'winner-takes-all' phenomena observed in competitive systems are thermodynamically distinct phases of the underlying evolving networks

Simulating the Storage and the Blockage Effects of Buildings in Urban Flood Modeling

Buildings often affect overland flow propagation in urban areas. Building walls change the direction and velocity of flow and can exclude interior spaces from flooding. However, water may intrude buildings when the flood level exceeds the height of protection. This study develops an inundation model that represents the resistance and the storage effects of buildings. This model was applied to central Taipei City, which is surrounded by the Danshui and Keelung Rivers. The inundation depth and extent were compared from models where the effects of buildings were included and excluded. Rainfall data from the Typhoon Nari event in 2001 was used in the simulation. The results showed that in the case where the effects of buildings were excluded inundation was underestimated in the metropolitan areas. Where the effects of buildings were considered in the model, the presented inundation model reproduces the inundation results more comparable with the observed flooding situation

Active Exploration for Robust Object Detection

Today, mobile robots are increasingly expected to operate in ever more complex and dynamic environments. In order to carry out many of the higher-level tasks envisioned a semantic understanding of a workspace is pivotal. Here our field has benefited significantly from successes in machine learning and vision: applications in robotics of off-the-shelf object detectors are plentiful. This paper outlines an online, any-time planning framework enabling the active exploration of such detections. Our approach exploits the ability to move to different vantage points and implicitly weighs the benefits of gaining more certainty about the existence of an object against the physical cost of the exploration required. The result is a robot which plans trajectories specifically to decrease the entropy of putative detections. Our system is demonstrated to significantly improve detection performance and trajectory length in simulated and real robot experiments.National Science Foundation (U.S.) (IIS grant 0546467)United States. Office of Naval Research (MURI N1141207-236214

Planning to Perceive: Exploiting Mobility for Robust Object Detection

Consider the task of a mobile robot autonomously navigating through an environment while detecting and mapping objects of interest using a noisy object detector. The robot must reach its destination in a timely manner, but is rewarded for correctly detecting recognizable objects to be added to the map, and penalized for false alarms. However, detector performance typically varies with vantage point, so the robot benefits from planning trajectories which maximize the efficacy of the recognition system. This work describes an online, any-time planning framework enabling the active exploration of possible detections provided by an off-the-shelf object detector. We present a probabilistic approach where vantage points are identified which provide a more informative view of a potential object. The agent then weighs the benefit of increasing its confidence against the cost of taking a detour to reach each identified vantage point. The system is demonstrated to significantly improve detection and trajectory length in both simulated and real robot experiments

Impact of intraoperative ocular lubricants on corneal debridement rate during vitreoretinal surgery

Purpose: To compare surgical parameters among patients receiving Viscoat (sodium chondroitin sulfate 4%-sodium hyaluronate 3%) or Goniosol (hydroxypropyl methylcellulose 2.5%) as topical lubricants for retinal surgery. Methods: This was a retrospective analysis of patients undergoing retinal surgery between March 2013 and March 2018 using Goniosol or Viscoat as adjuvants. Primary outcome measures were rate of corneal debridement and operative time between groups, compared using Results: Compared to Viscoat (n=319), the Goniosol group (n=210) had more frequent intraoperative corneal debridement (21.4% vs 0, Conclusion: These findings suggest potential advantages of using Viscoat over Goniosol for corneal lubrication to aid visualization during vitreoretinal surgery

Investigation of CTA 1 with Suzaku Observation

We report on an 105 ks Suzaku observation of the supernova remnant CTA 1 (G119.5+10.2). The Suzaku soft X-ray observation was carried out with both timing mode and imaging mode. A ~ 10' extended feature, which is interpreted as a bow-shock component of the pulsar wind nebula (PWN), is revealed in this deep observation for the first time. The nebular spectrum can be modelled by a power-law with a photon index of ~ 1.8 which suggests a slow synchrotron cooling scenario. The photon index is approximately constant across this extended feature. We compare and discuss our observations of this complex nebula with previous X-ray investigations. We do not obtain any significant pulsation from the central pulsar in the soft (0.2-12 keV) and hard (10-60 keV) X-ray data. The non-detection is mainly due to the loss of the precise imaging ability to accurately determine the source contribution. The spectra of XIS and HXD can be directly connected without a significant spectral break according to our analysis. Future observations of NuSTAR and Astro-H would be able to resolve the contamination and provide an accurate hard X-ray measurement of CTA 1.Comment: 9 pages, 7 figures, accepted by MNRA

Probabilistic lane estimation for autonomous driving using basis curves

Lane estimation for autonomous driving can be formulated as a curve estimation problem, where local sensor data provides partial and noisy observations of spatial curves forming lane boundaries. The number of lanes to estimate are initially unknown and many observations may be outliers or false detections (due e.g. to shadows or non-boundary road paint). The challenges lie in detecting lanes when and where they exist, and updating lane estimates as new observations are made. This paper describes an efficient probabilistic lane estimation algorithm based on a novel curve representation. The key advance is a principled mechanism to describe many similar curves as variations of a single basis curve. Locally observed road paint and curb features are then fused to detect and estimate all nearby travel lanes. The system handles roads with complex multi-lane geometries and makes no assumptions about the position and orientation of the vehicle with respect to the roadway. We evaluate our algorithm using a ground truth dataset containing manually-labeled, fine-grained lane geometries for vehicle travel in two large and diverse datasets that include more than 300,000 images and 44 km of roadway. The results illustrate the capabilities of our algorithm for robust lane estimation in the face of challenging conditions and unknown roadways.United States. Defense Advanced Research Projects Agency (Urban Challenge, ARPA Order No. W369/00, Program Code DIRO, issued by DARPA/CMO under Contract No. HR0011-06-C-0149

A High-Rate, Heterogeneous Data Set from the Darpa Urban Challenge

This paper describes a data set collected by MIT’s autonomous vehicle Talos during the 2007 DARPA Urban Challenge. Data from a high-precision navigation system, five cameras, 12 SICK planar laser range scanners, and a Velodyne high-density laser range scanner were synchronized and logged to disk for 90 km of travel. In addition to documenting a number of large loop closures useful for developing mapping and localization algorithms, this data set also records the first robotic traffic jam and two autonomous vehicle collisions. It is our hope that this data set will be useful to the autonomous vehicle community, especially those developing robotic perception capabilities.United States. Defense Advanced Research Projects Agency (Urban Challenge, ARPA Order No. W369/00, Program Code DIRO, issued by DARPA/CMO under Contract No. HR0011-06-C-0149