Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization

Many robotics applications require precise pose estimates despite operating in large and changing environments. This can be addressed by visual localization, using a pre-computed 3D model of the surroundings. The pose estimation then amounts to finding correspondences between 2D keypoints in a query image and 3D points in the model using local descriptors. However, computational power is often limited on robotic platforms, making this task challenging in large-scale environments. Binary feature descriptors significantly speed up this 2D-3D matching, and have become popular in the robotics community, but also strongly impair the robustness to perceptual aliasing and changes in viewpoint, illumination and scene structure. In this work, we propose to leverage recent advances in deep learning to perform an efficient hierarchical localization. We first localize at the map level using learned image-wide global descriptors, and subsequently estimate a precise pose from 2D-3D matches computed in the candidate places only. This restricts the local search and thus allows to efficiently exploit powerful non-binary descriptors usually dismissed on resource-constrained devices. Our approach results in state-of-the-art localization performance while running in real-time on a popular mobile platform, enabling new prospects for robotics research.Comment: CoRL 2018 Camera-ready (fix typos and update citations

Stacking-based visualization of trajectory attribute data

Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well

Fidelity-Weighted Learning

Training deep neural networks requires many training samples, but in practice training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing. This creates a fundamental quality versus-quantity trade-off in the learning process. Do we learn from the small amount of high-quality data or the potentially large amount of weakly-labeled data? We argue that if the learner could somehow know and take the label-quality into account when learning the data representation, we could get the best of both worlds. To this end, we propose "fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach for training deep neural networks using weakly-labeled data. FWL modulates the parameter updates to a student network (trained on the task we care about) on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher (who has access to the high-quality labels). Both student and teacher are learned from the data. We evaluate FWL on two tasks in information retrieval and natural language processing where we outperform state-of-the-art alternative semi-supervised methods, indicating that our approach makes better use of strong and weak labels, and leads to better task-dependent data representations.Comment: Published as a conference paper at ICLR 201

Partitioned Blockmap Indexes for Multidimensional Data Access

Given recent increases in the size of main memory in modern machines, it is now common to to store large data sets in RAM for faster processing. Multidimensional access methods aim to provide efficient access to large data sets when queries apply predicates to some of the data dimensions. We examine multidimensional access methods in the context of an in-memory column store tuned for on-line analytical processing or scientific data analysis. We propose a multidimensional data structure that contains a novel combination of a grid array and several bitmaps. The base data is clustered in an order matching that of the index structure. The bitmaps contain one bit per block of data, motivating the term "blockmap." The proposed data structures are compact, typically taking less than one bit of space per row of data. Partition boundaries can be chosen in a way that reflects both the query workload and the data distribution, and boundaries are not required to evenly divide the data if there is a bias in the query distribution. We examine the theoretical performance of the data structure and experimentally measure its performance on three modern CPUs and one GPU processor. We demonstrate that efficient multidimensional access can be achieved with minimal space overhead

SST: Integrated Fluorocarbon Microsensor System Using Catalytic Modification

Selective, sensitive, and reliable sensors are urgently needed to detect air-borne halogenated volatile organic compounds (VOCs). This broad class of compounds includes chlorine, fluorine, bromine, and iodine containing hydrocarbons used as solvents, refrigerants, herbicides, and more recently as chemical warfare agents (CWAs). It is important to be able to detect very low concentrations of halocarbon solvents and insecticides because of their acute health effects even in very low concentrations. For instance, the nerve agent sarin (isopropyl methylphosphonofluoridate), first developed as an insecticide by German chemists in 1938, is so toxic that a ten minute exposure at an airborne concentration of only 65 parts per billion (ppb) can be fatal. Sarin became a household term when religious cult members on Tokyo subway trains poisoned over 5,500 people, killing 12. Sarin and other CWAs remain a significant threat to the health and safety of the general public. The goal of this project is to design a sensor system to detect and identify the composition and concentration of fluorinated VOCs. The system should be small, robust, compatible with metal oxide semiconductor (MOS) technology, cheap, if produced in large scale, and has the potential to be versatile in terms of low power consumption, detection of other gases, and integration in a portable system. The proposed VOC sensor system has three major elements that will be integrated into a microreactor flow cell: a temperature-programmable microhotplate array/reactor system which serves as the basic sensor platform; an innovative acoustic wave sensor, which detects material removal (instead of deposition) to verify and quantify the presence of fluorine; and an intelligent method, support vector machines, that will analyze the complex and high dimensional data furnished by the sensor system. The superior and complementary aspects of the three elements will be carefully integrated to create a system which is more sensitive and selective than other CWA detection systems that are commercially available or described in the research literature. While our sensor system will be developed to detect fluorinated VOCs, it can be adapted for other applications in which a target analyte can be catalytically converted for selective detection. Therefore, this investigation will examine the relationships between individual sensor element performance and joint sensor platform performance, integrated with state-of-the-art data analysis techniques. During development of the sensor system, the investigators will consider traditional reactor design concepts such as mass transfer and residence time effects, and will apply them to the emerging field of microsystems. The proposed research will provide the fundamental basis and understanding for examining multifunctional sensor platforms designed to provide extreme selectivity to targeted molecules. The project will involve interdisciplinary researchers and students and will connect to K-12 and RET programs for underrepresented students from rural areas

Towards an Instance-Optimal Z-Index

We present preliminary results on instance-optimal variants of the Z-index, a well-known spatial index that makes use of the Z-order curve. Unlike the base Z-index, the variants we propose aim to adapt to the data and range-query workloads of the given setting. Specifically, we provide an optimal algorithm that builds a Z-index that minimizes the expected number of retrieved data points for the given data and query workload. Moreover, since the optimal algorithm requires supra-linear running time, we additionally propose efficient heuristic algorithms to use in its place. Our experiments evaluate the performance of the resultant Z-indexes.Peer reviewe