Graph-based Proprioceptive Localization Using a Discrete Heading-Length Feature Sequence Matching Approach

Proprioceptive localization refers to a new class of robot egocentric localization methods that do not rely on the perception and recognition of external landmarks. These methods are naturally immune to bad weather, poor lighting conditions, or other extreme environmental conditions that may hinder exteroceptive sensors such as a camera or a laser ranger finder. These methods depend on proprioceptive sensors such as inertial measurement units (IMUs) and/or wheel encoders. Assisted by magnetoreception, the sensors can provide a rudimentary estimation of vehicle trajectory which is used to query a prior known map to obtain location. Named as graph-based proprioceptive localization (GBPL), we provide a low cost fallback solution for localization under challenging environmental conditions. As a robot/vehicle travels, we extract a sequence of heading-length values for straight segments from the trajectory and match the sequence with a pre-processed heading-length graph (HLG) abstracted from the prior known map to localize the robot under a graph-matching approach. Using the information from HLG, our location alignment and verification module compensates for trajectory drift, wheel slip, or tire inflation level. We have implemented our algorithm and tested it in both simulated and physical experiments. The algorithm runs successfully in finding robot location continuously and achieves localization accurate at the level that the prior map allows (less than 10m).Comment: 13 pages, 32 figure

LoopSmart: Smart Visual SLAM Through Surface Loop Closure

We present a visual simultaneous localization and mapping (SLAM) framework of closing surface loops. It combines both sparse feature matching and dense surface alignment. Sparse feature matching is used for visual odometry and globally camera pose fine-tuning when dense loops are detected, while dense surface alignment is the way of closing large loops and solving surface mismatching problem. To achieve smart dense surface loop closure, a highly efficient CUDA-based global point cloud registration method and a map content dependent loop verification method are proposed. We run extensive experiments on different datasets, our method outperforms state-of-the-art ones in terms of both camera trajectory and surface reconstruction accuracy

Modeling Varying Camera-IMU Time Offset in Optimization-Based Visual-Inertial Odometry

Combining cameras and inertial measurement units (IMUs) has been proven effective in motion tracking, as these two sensing modalities offer complementary characteristics that are suitable for fusion. While most works focus on global-shutter cameras and synchronized sensor measurements, consumer-grade devices are mostly equipped with rolling-shutter cameras and suffer from imperfect sensor synchronization. In this work, we propose a nonlinear optimization-based monocular visual inertial odometry (VIO) with varying camera-IMU time offset modeled as an unknown variable. Our approach is able to handle the rolling-shutter effects and imperfect sensor synchronization in a unified way. Additionally, we introduce an efficient algorithm based on dynamic programming and red-black tree to speed up IMU integration over variable-length time intervals during the optimization. An uncertainty-aware initialization is also presented to launch the VIO robustly. Comparisons with state-of-the-art methods on the Euroc dataset and mobile phone data are shown to validate the effectiveness of our approach.Comment: European Conference on Computer Vision 201

Inference Control for Privacy-Preserving Genome Matching

Privacy is of the utmost importance in genomic matching. Therefore a number of privacy-preserving protocols have been presented using secure computation. Nevertheless, none of these protocols prevents inferences from the result. Goodrich has shown that this resulting information is sufficient for an effective attack on genome databases. In this paper we present an approach that can detect and mitigate such an attack on encrypted messages while still preserving the privacy of both parties. Note that randomization, e.g.~using differential privacy, will almost certainly destroy the utility of the matching result. We combine two known cryptographic primitives -- secure computation of the edit distance and fuzzy commitments -- in order to prevent submission of similar genome sequences. Particularly, we contribute an efficient zero-knowledge proof that the same input has been used in both primitives. We show that using our approach it is feasible to preserve privacy in genome matching and also detect and mitigate Goodrich's attack.Comment: 20 pages, 4 figure

Performance Analysis and Robustification of Single-query 6-DoF Camera Pose Estimation

We consider a single-query 6-DoF camera pose estimation with reference images and a point cloud, i.e. the problem of estimating the position and orientation of a camera by using reference images and a point cloud. In this work, we perform a systematic comparison of three state-of-the-art strategies for 6-DoF camera pose estimation, i.e. feature-based, photometric-based and mutual-information-based approaches. The performance of the studied methods is evaluated on two standard datasets in terms of success rate, translation error and max orientation error. Building on the results analysis, we propose a hybrid approach that combines feature-based and mutual-information-based pose estimation methods since it provides complementary properties for pose estimation. Experiments show that (1) in cases with large environmental variance, the hybrid approach outperforms feature-based and mutual-information-based approaches by an average of 25.1% and 5.8% in terms of success rate, respectively; (2) in cases where query and reference images are captured at similar imaging conditions, the hybrid approach performs similarly as the feature-based approach, but outperforms both photometric-based and mutual-information-based approaches with a clear margin; (3) the feature-based approach is consistently more accurate than mutual-information-based and photometric-based approaches when at least 4 consistent matching points are found between the query and reference images

Fault Tolerance in Distributed Systems using Fused State Machines

Replication is a standard technique for fault tolerance in distributed systems modeled as deterministic finite state machines (DFSMs or machines). To correct f crash or f/2 Byzantine faults among n different machines, replication requires nf additional backup machines. We present a solution called fusion that requires just f additional backup machines. First, we build a framework for fault tolerance in DFSMs based on the notion of Hamming distances. We introduce the concept of an (f,m)-fusion, which is a set of m backup machines that can correct f crash faults or f/2 Byzantine faults among a given set of machines. Second, we present an algorithm to generate an (f,f)-fusion for a given set of machines. We ensure that our backups are efficient in terms of the size of their state and event sets. Our evaluation of fusion on the widely used MCNC'91 benchmarks for DFSMs show that the average state space savings in fusion (over replication) is 38% (range 0-99%). To demonstrate the practical use of fusion, we describe its potential application to the MapReduce framework. Using a simple case study, we compare replication and fusion as applied to this framework. While a pure replication-based solution requires 1.8 million map tasks, our fusion-based solution requires only 1.4 million map tasks with minimal overhead during normal operation or recovery. Hence, fusion results in considerable savings in state space and other resources such as the power needed to run the backup tasks.Comment: This is under review with the Distributed Computing journa

Camera Relocalization by Computing Pairwise Relative Poses Using Convolutional Neural Network

We propose a new deep learning based approach for camera relocalization. Our approach localizes a given query image by using a convolutional neural network (CNN) for first retrieving similar database images and then predicting the relative pose between the query and the database images, whose poses are known. The camera location for the query image is obtained via triangulation from two relative translation estimates using a RANSAC based approach. Each relative pose estimate provides a hypothesis for the camera orientation and they are fused in a second RANSAC scheme. The neural network is trained for relative pose estimation in an end-to-end manner using training image pairs. In contrast to previous work, our approach does not require scene-specific training of the network, which improves scalability, and it can also be applied to scenes which are not available during the training of the network. As another main contribution, we release a challenging indoor localisation dataset covering 5 different scenes registered to a common coordinate frame. We evaluate our approach using both our own dataset and the standard 7 Scenes benchmark. The results show that the proposed approach generalizes well to previously unseen scenes and compares favourably to other recent CNN-based methods

Robust Keystroke Biometric Anomaly Detection

The Keystroke Biometrics Ongoing Competition (KBOC) presented an anomaly detection challenge with a public keystroke dataset containing a large number of subjects and real-world aspects. Over 300 subjects typed case-insensitive repetitions of their first and last name, and as a result, keystroke sequences could vary in length and order depending on the usage of modifier keys. To deal with this, a keystroke alignment preprocessing algorithm was developed to establish a semantic correspondence between keystrokes in mismatched sequences. The method is robust in the sense that query keystroke sequences need only approximately match a target sequence, and alignment is agnostic to the particular anomaly detector used. This paper describes the fifteen best-performing anomaly detection systems submitted to the KBOC, which ranged from auto-encoding neural networks to ensemble methods. Manhattan distance achieved the lowest equal error rate of 5.32%, while all fifteen systems performed better than any other submission. Performance gains are shown to be due in large part not to the particular anomaly detector, but to preprocessing and score normalization techniques

An Active RBSE Framework to Generate Optimal Stimulus Sequences in a BCI for Spelling

A class of brain computer interfaces (BCIs) employs noninvasive recordings of electroencephalography (EEG) signals to enable users with severe speech and motor impairments to interact with their environment and social network. For example, EEG based BCIs for typing popularly utilize event related potentials (ERPs) for inference. Presentation paradigm design in current ERP-based letter by letter typing BCIs typically query the user with an arbitrary subset characters. However, the typing accuracy and also typing speed can potentially be enhanced with more informed subset selection and flash assignment. In this manuscript, we introduce the active recursive Bayesian state estimation (active-RBSE) framework for inference and sequence optimization. Prior to presentation in each iteration, rather than showing a subset of randomly selected characters, the developed framework optimally selects a subset based on a query function. Selected queries are made adaptively specialized for users during each intent detection. Through a simulation-based study, we assess the effect of active-RBSE on the performance of a language-model assisted typing BCI in terms of typing speed and accuracy. To provide a baseline for comparison, we also utilize standard presentation paradigms namely, row and column matrix presentation paradigm and also random rapid serial visual presentation paradigms. The results show that utilization of active-RBSE can enhance the online performance of the system, both in terms of typing accuracy and speed.Comment: 10 pages, 6 figures, Will be submitted to IEEE transactions on Signal Processin

Chinese Spelling Error Detection Using a Fusion Lattice LSTM

Spelling error detection serves as a crucial preprocessing in many natural language processing applications. Due to the characteristics of Chinese Language, Chinese spelling error detection is more challenging than error detection in English. Existing methods are mainly under a pipeline framework, which artificially divides error detection process into two steps. Thus, these methods bring error propagation and cannot always work well due to the complexity of the language environment. Besides existing methods only adopt character or word information, and ignore the positive effect of fusing character, word, pinyin1 information together. We propose an LF-LSTM-CRF model, which is an extension of the LSTMCRF with word lattices and character-pinyin-fusion inputs. Our model takes advantage of the end-to-end framework to detect errors as a whole process, and dynamically integrates character, word and pinyin information. Experiments on the SIGHAN data show that our LF-LSTM-CRF outperforms existing methods with similar external resources consistently, and confirm the feasibility of adopting the end-to-end framework and the availability of integrating of character, word and pinyin information.Comment: 8 pages,5 figure