Bearing-only visual SLAM for small unmanned aerial vehicles in GPS-denied environments

This paper presents a hierarchical simultaneous localization and mapping(SLAM) system for a small unmanned aerial vehicle(UAV) using the output of an inertial measurement unit(IMU) and the bearing-only observations from an onboard monocular camera.A homography based approach is used to calculate the motion of the vehicle in 6 degrees of freedom by image feature match.This visual measurement is fused with the inertial outputs by an indirect extended Kalman filter(EKF) for attitude and velocity estimation.Then,another EKF is employed to estimate the position of the vehicle and the locations of the features in the map.Both simulations and experiments are carried out to test the performance of the proposed system.The result of the comparison with the referential global positioning system/inertial navigation system(GPS/INS) navigation indicates that the proposed SLAM can provide reliable and stable state estimation for small UAVs in GPS-denied environments.     

Compressed sensing SAR imaging based on sparse representation in fractional Fourier domain

Compressed sensing(CS)is a new technique of utilizing a priori knowledge on sparsity of data in a certain domain for minimizing necessary number of measurements.Based on this idea,this paper proposes a novel synthetic aperture radar(SAR)imaging approach by exploiting sparseness of echo data in the fractional Fourier domain.The effectiveness and robustness of the approach are assessed by some numerical experiments under various noisy conditions and different measurement matrices.Experimental results have shown that,the obtained images by using the CS technique depend on measurement matrix and have higher output signal to noise ratio than traditional pulse compression technique.Finally simulated and real data are also processed and the achieved results show that the proposed approach is capable of reconstructing the image of targets and effectively suppressing noise.     

A Fusion Kalman Filter and UFIR Estimator Using the Influence Function Method

In this paper, the Kalman filter(KF) and the unbiased finite impulse response(UFIR) filter are fused in the discrete-time state-space to improve robustness against uncertainties. To avoid the problem where fusion filters may give up some advantages of UFIR filters by fusing based on noise statistics, we attempt to find a way to fuse without using noise statistics. The fusion filtering algorithm is derived using the influence function that provides a quantified measure for disturbances on the res...     

Comparison of different pseudo-linear estimators for vision-based target motion estimation

Vision-based target motion estimation based Kalman filtering or least-squares estimators is an important problem in many tasks such as vision-based swarming or vision-based target pursuit. In this paper, we focus on a problem that is very specific yet we believe important. That is, from the vision measurements, we can formulate various measurements. Which and how the measurements should be used? These problems are very fundamental, but we notice that practitioners usually do not pay special attention to them and often make mistakes. Motivated by this, we formulate three pseudo-linear measurements based on the bearing and angle measurements, which are standard vision measurements that can be obtained. Different estimators based on Kalman filtering and least-squares estimation are established and compared based on numerical experiments. It is revealed that correctly analyzing the covariance noises is critical for the Kalman filtering-based estimators. When the variance of the original measurement noise is unknown, the pseudo-linear least-squares estimator that has the smallest magnitude of the transformed noise can be a good choice.     

Distributed adaptive Kalman filter based on variational Bayesian technique

In this paper, distributed Kalman filter design is studied for linear dynamics with unknown measurement noise variance, which modeled by Wishart distribution. To solve the problem in a multi-agent network, a distributed adaptive Kalman filter is proposed with the help of variational Bayesian, where the posterior distribution of joint state and noise variance is approximated by a free-form distribution. The convergence of the proposed algorithm is proved in two main steps: noise statistics is estimated, where each agent only use its local information in variational Bayesian expectation (VB-E) step, and state is estimated by a consensus algorithm in variational Bayesian maximum (VB-M) step. Finally, a distributed target tracking problem is investigated with simulations for illustration.     

Computational Complexity and Anytime Algorithm for Inconsistency Measurement

Measuring inconsistency degrees of inconsistent knowledge bases is an important problem as it provides context information for facilitating inconsistency handling. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. In this paper, we consider the computational aspects of inconsistency degrees of propositional knowledge bases under 4-valued semantics. We ˉrst give a complete analysis of the computational complexity of computing inconsistency degrees. As it turns out that computing the exact inconsistency degree is intractable, we then propose an anytime algorithm that provides tractable approximations of the inconsistency degree from above and below. We show that our algorithm satisˉes some desirable properties and give experimental results of our implementation of the algorithm.     

Crosstalk-Aware Routing Resource Assignment

Crosstalk noise is one of the emerging issues in deep sub-micrometer technology which causes many undesired effects on the circuit performance. In this paper, a Crosstalk-Aware Routing Resource Assignment (CARRA) algorithm is proposed, which integrates the routing layers and tracks to address the crosstalk noise issue during the track/layer assignment stage. The CARRA problem is formulated as a weighted bipartite matching problem and solved using the linear assignment algorithm. The crosstalk risks between nets are represented by an undirected graph and the maximum number of the concurrent crosstalk risking nets is computed as the max clique of the graph. Then the nets in each max clique are assigned to disadjacent tracks. Thus the crosstalk noise can be avoided based on the clique concept. The algorithm is tested on IBM benchmarks and the experimental results show that it can improve the final routing layout a lot with little loss of the completion rate.     

Discrete-time inverse linear quadratic optimal control over finite time-horizon under noisy output measurements

In this paper, the problem of inverse quadratic optimal control over finite time-horizon for discrete-time linear systems is considered. Our goal is to recover the corresponding quadratic objective function using noisy observations. First, the identifiability of the model structure for the inverse optimal control problem is analyzed under relative degree assumption and we show the model structure is strictly globally identifiable. Next, we study the inverse optimal control problem whose initial state distribution and the observation noise distribution are unknown, yet the exact observations on the initial states are available. We formulate the problem as a risk minimization problem and approximate the problem using empirical average. It is further shown that the solution to the approximated problem is statistically consistent under the assumption of relative degrees. We then study the case where the exact observations on the initial states are not available, yet the observation noises are known to be white Gaussian distributed and the distribution of the initial state is also Gaussian (with unknown mean and covariance). EM-algorithm is used to estimate the parameters in the objective function. The effectiveness of our results are demonstrated by numerical examples.     

Consensus controller for multi-UAV navigation

In this paper, we design consensus algorithms for multiple unmanned aerial vehicles (UAV). We mainly focus on the control design in the face of measurement noise and propose a position consensus controller based on the sliding mode control by using the distributed UAV information. Within the framework of Lyapunov theory, it is shown that all signals in the closed-loop multi-UAV systems are stabilized by the proposed algorithm, while consensus errors are uniformly ultimately bounded. Moreover, for each local UAV, we propose a mechanism to define the trustworthiness, based on which the edge weights are tuned to eliminate negative influence from stubborn agents or agents exposed to extremely noisy measurement. Finally, we develop software for a nano UAV platform, based on which we implement our algorithms to address measurement noises in UAV flight tests. The experimental results validate the effectiveness of the proposed algorithms.     

A two-stage heuristic method for vehicle routing problem with split deliveries and pickups

The vehicle routing problem （VRP） is a well-known combinatorial optimization issue in transportation and logistics network systems. There exist several limitations associated with the traditional VRP. Releasing the restricted conditions of traditional VRP has become a research focus in the past few decades. The vehicle routing problem with split deliveries and pickups （VRPSPDP） is particularly proposed to release the constraints on the visiting times per customer and vehicle capacity, that is, to allow the deliveries and pickups for each customer to be simultaneously split more than once. Few studies have focused on the VRPSPDP problem. In this paper we propose a two-stage heuristic method integrating the initial heuristic algorithm and hybrid heuristic algorithm to study the VRPSPDP problem. To validate the proposed algorithm, Solomon benchmark datasets and extended Solomon benchmark datasets were modified to compare with three other popular algorithms. A total of 18 datasets were used to evaluate the effectiveness of the proposed method. The computational results indicated that the proposed algorithm is superior to these three algorithms for VRPSPDP in terms of total travel cost and average loading rate.     

一种改进的FastSLAM算法

同时定位与建图是移动机器人实现真正自治的必要前提,FastSLAM作为一种成功的SLAM方法受到研究者的广泛青睐,FastSLAM将SLAM问题分为一个定位问题和一个建图问题,其中用扩展卡尔曼滤波器（EKF）实现地图陆标的估计与更新,提出了一种改进的FastSLAM方法,用UKF滤波器代替EKF实现FastSLAM中的陆标估计,使得陆标的估计精度提高,该方法同时具有UKF滤波器无需求解观测模型的雅克比矩阵的优点。     

基于组合EKF的自主水下航行器SLAM

针对标准扩展卡尔曼滤波(EKF)在噪声统计特性不准确、系统模型与实际模型无法完全匹配情况下滤波精度严重下降的问题,提出了一种基于Sage-Husa自适应EKF和强跟踪EKF组合的SLAM(同步定位与地图构建)算法.首先建立了AUV(自主水下航行器)的动力学模型、特征模型以及传感器的测量模型,然后通过Hough变换进行特征提取,最终采用组合EKF实现了自主水下航行器的同步定位与地图构建.海试数据仿真试验表明本文所提方法降低了噪声统计特性时变以及模型不精确对系统的影响,提高了SLAM系统的精确性和鲁棒性.     

Experimental Study of Iterated Kalman Filters for Simultaneous Localization and Mapping of Autonomous Mobile Robots

In this paper, we investigate the role of iteration in Kalman filters family for improvement of the estimation accuracy of states in simultaneous localization and mapping (SLAM). The linearized error propagation existing in Kalman filters family can result in large errors and inconsistency in the SLAM problem. One approach to alleviate this situation is the use of iteration in extended Kalman filter (EKF) and sigma point Kalman filter (SPKF) based SLAM. The main contribution is to present that the iterated versions of Kalman filters can increase consistency and robustness of these filters against linear error propagation. Experimental results are presented to validate this improvement of state estimate convergence through repetitive linearization of the nonlinear observation model in EKF-SLAM and SPKF-SLAM algorithms.     

Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM

This paper investigates the convergence properties and consistency of extended Kalman filter (EKF) based simultaneous localization and mapping (SLAM) algorithms. Proofs of convergence are provided for the nonlinear two-dimensional SLAM problem with point landmarks observed using a range-and-bearing sensor. It is shown that the robot orientation uncertainty at the instant when landmarks are first observed has a significant effect on the limit and/or the lower bound of the uncertainties of the landmark position estimates. This paper also provides some insights to the inconsistencies of EKF based SLAM that have been recently observed. The fundamental cause of EKF SLAM inconsistency for two basic scenarios are clearly stated and associated theoretical proofs are provided.     

基于EKF的SLAM算法的一致性分析

基于EKF的SLAM算法在应用中已取得了很大的成功,但其估计算法的一致性问题却没有得到很好的解决.为了分析导致SLAM算法不一致的原因,结合自主移动机器人的仿真模型及一致性估计的一般判据,对该算法一致性成立的条件,及导致不一致的原因进行了全面的理论分析和仿真研究.理论分析指出导致EKF-SLAM算法不一致的原因在于EKF非线性算法引起的误差积累.仿真研究表明出现不一致的本质原因在于移动机器人姿态角的误差和不确定性.当姿态角的误差超过一定限度,就会导致EKF-SLAM算法不一致.研究结果表明,提高EKF-SLAM算法一致性的关键在于降低对姿态角估计的不确定性.     

Consistency of SLAM-EKF Algorithms for Indoor Environments

The solution to the Simultaneous Localization And Mapping (SLAM) problem using an Extended Kalman Filter (EKF) is probably the most extended in the literature despite the recently reported inconsistency of its estimation. There has been an important lack of successful SLAM-EKF implementations for indoor environments that could build monolithic large maps with features conveying angular information. In this paper we analyze the source and factors of the SLAM-EKF inconsistency in indoor environments (where the landmarks contain angular information) and we review current existing approaches presenting novel solutions to this problem that let us build indoor large monolithic feature based maps.     

Local map fusion for real‐time indoor simultaneous localization and mapping

Among the solutions to the simultaneous localization and mapping (SLAM) problem with probabilistic techniques, the extended Kalman filter (EKF) is a very common approach. There are several approaches to deal with its computational cost, usually based on an adequate selection of features to be updated in real time, while the whole map update is delayed or processed in a background task, allowing one to map larger environments or to carry out multirobot experiments. Although these solutions are theoretically sound, there is a great lack of real experiments in large indoor environments due to several previously unknown problems derived from the geometric model of the map features and the inconsistency of the SLAM‐EKF algorithm. For the first time, these problems are described and solved, and the implementation of the algorithms and solutions presented in this paper achieve excellent results in experiments in different real large indoor environments. © 2006 Wiley Periodicals, Inc.     

基于MIEKF的移动机器人同时定位与地图构建研究*

由于移动机器人处在未知并且不确定的环境中,主要采用基于概率的方法对同时定位与地图构建(SLAM)进行描述。本文建立了SLAM问题的概率表示模型,并对在解决SLAM问题中用最常用的扩展卡尔曼滤波(EKF)算法以及迭代扩展卡尔曼滤波(IEKF)算法进行描述。本文针对两种算法的缺陷和不足,将应用于跟踪领域的修正迭代扩展卡尔曼滤波算法(MIEKF)与SLAM思想结合,提出了一种新的基于MIEKF的SLAM算法。通过基于点特征的SLAM实验验证了该算法的有效性。     

一种基于RBUKF滤波器的SLAM算法一种基于RBUKF滤波器的SLAM算法

同时定位与建图(SLAM)是智能机器人实现真正自治的必要前提,是一个比单独研究定位或者建图更加困难的课题。该文将基于SUT变换的RBUKF滤波器应用于平面静态环境下的同时定位与建图算法,它能够在同样计算复杂度的情况下,避免基于扩展卡尔曼滤波器(EKF)SLAM算法由于线性化误差大导致滤波器发散,从而出现建图错误的缺点。基于公共数据集的实验表明该方法估计的最终地图比EKF的方法精度高。     

Monocular SLAM with undelayed initialization for an indoor robot

This paper presents a new feature initialization method for monocular EKF SLAM (Extended Kalman Filter Simultaneous Localization and Mapping) which utilizes a 3D measurement model in the camera frame rather than 2D pixel coordinates in the image plane. The key idea is to regard a camera as a range and bearing sensor, of which the range information contains numerous uncertainties. 2D pixel coordinates of measurement are converted to 3D points in the camera frame with an assumed depth. The element of the measurement noise covariance corresponding to the depth of the feature is set to a very high value. And it is shown that the proposed measurement model has very little linearization error, which can be critical for the EKF performance. Furthermore, this paper proposes an EKF SLAM system that combines odometry, a low-cost gyro, and low frame rate (1–2 Hz) monocular vision. Low frame rate is crucial for reducing the price of the processor. This system combination is cost-effective enough to be commercialized for a real vacuum cleaning application. Simulations and experimental results show the efficacy of the proposed method with computational efficiency in indoor environments.