Multisensor-Based Human Detection and Tracking for Mobile Service Robots

One of fundamental issues for service robots is human-robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In this paper, we propose a solution for human tracking with a mobile robot that implements multisensor data fusion techniques. The system utilizes a new algorithm for laser-based leg detection using the onboard laser range finder (LRF). The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to also be very discriminative in cluttered environments. These patterns can be used to localize both static and walking persons, even when the robot moves. Furthermore, faces are detected using the robot's camera, and the information is fused to the legs' position using a sequential implementation of unscented Kalman filter. The proposed solution is feasible for service robots with a similar device configuration and has been successfully implemented on two different mobile platforms. Several experiments illustrate the effectiveness of our approach, showing that robust human tracking can be performed within complex indoor environments.     

Robust unscented Kalman filter with adaptation of process and measurement noise covariances

Unscented Kalman filter (UKF) has been extensively used for state estimation of nonlinear stochastic systems, which suffers from performance degradation and even divergence when the noise distribution used in the UKF and the truth in a real system are mismatched. For state estimation of nonlinear stochastic systems with non-Gaussian measurement noise, the Masreliez–Martin extended Kalman filter (EKF) gives better state estimates in relation to the standard EKF. However, the process noise and the measurement noise covariance matrices should be known, which is impractical in applications. This paper presents a robust Masreliez–Martin UKF which can provide reliable state estimates in the presence of both unknown process noise and measurement noise covariance matrices. Two numerical examples involving relative navigation of spacecrafts demonstrate that the proposed filter can provide improved state estimation performance over existing robust filtering approaches. Vision-aided robot arm tracking experiments are also provided to show the effectiveness of the proposed approach.     

面向服务机器人的多行人检测与跟踪

服务机器人在给人提供帮助,带来生活便利的同时,需要检测并跟踪行人.然而,环境复杂,多个行人之间存在遮挡等问题,给行人的检测与跟踪带来了挑战.在行人检测方面,本文提出了最近邻方法融合激光人腿检测和Kinect人体检测的结果,有效改善了行人检测的精度和完整性.针对多行人跟踪,本文提出了一种改进的粒子滤波算法对行人的位置和速度进行了估计,克服了传统粒子滤波算法计算量大,重采样阶段粒子贫化的缺点.最后,在实际场景中采用改造的turtlebot机器人进行了测试,并进行了计算机可视化,实验结果证明本文提出的方法具有很好的准确性,实时性和鲁棒性.     

Simultaneous people tracking and motion pattern learning

The field of Human Robot Interaction (HRI) encompasses many difficult challenges as robots need a better understanding of human actions. Human detection and tracking play a major role in such scenarios. One of the main challenges is to track them with long term occlusions due to agile nature of human navigation. However, in general humans do not make random movements. They tend to follow common motion patterns depending on their intentions and environmental/physical constraints. Therefore, knowledge of such common motion patterns could allow a robotic device to robustly track people even with long term occlusions. On the other hand, once a robust tracking is achieved, they can be used to enhance common motion pattern models allowing robots to adapt to new motion patterns that could appear in the environment. Therefore, this paper proposes to learn human motion patterns based on Sampled Hidden Markov Model (SHMM) and simultaneously track people using a particle filter tracker. The proposed simultaneous people tracking and human motion pattern learning has not only improved the tracking robustness compared to more conservative approaches, it has also proven robustness to prolonged occlusions and maintaining identity. Furthermore, the integration of people tracking and on-line SHMM learning have led to improved learning performance. These claims are supported by real world experiments carried out on a robot with suite of sensors including a laser range finder.     

Human-following robot using the particle filter in ISpace with distributed vision sensors

We present a method for representing tracking and human-following by fusing distributed multiple vision systems in intelligent space, with applications to pedestrian tracking in a crowd. In this context, particle filters provide a robust tracking framework under ambiguous conditions. The particle filter technique is used in this work, but in order to reduce its computational complexity and increase its robustness, we propose to track the moving objects by generating hypotheses not in the image plan but on a top-view reconstruction of the scene. Comparative results on real video sequences show the advantage of our method for multiobject tracking. Simulations are carried out to evaluate the proposed performance. Also, the method is applied to the intelligent environment, and its performance is verified by experiments. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

Fast and robust laser stripe extraction for 3D reconstruction in industrial environments

The use of 3D reconstruction based on active laser triangulation techniques is very complex in industrial environments. The main problem is that most of these techniques are based on laser stripe extraction methods which are highly sensitive to noise, which is virtually inevitable in these conditions. In industrial environments, variable luminance, reflections which show up in the images as noise, and uneven surfaces are common. These factors modify the shape of the laser profile. This work proposes a fast, accurate, and robust method to extract laser stripes in industrial environments. Specific procedures are proposed to extract the laser stripe projected on the background, using a boundary linking process, and on the foreground, using an improved Split-and-Merge approach with different approximation functions including linear, quadratic, and Akima splines. Also, a novel procedure to automatically define the region of interest in the image is proposed. The real-time performance of the proposed method is analyzed by measuring the time taken by the tasks involved in their application. Finally, the proposed extraction method is applied to two real applications: 3D reconstruction of steel strips and weld seam tracking.     

基于混杂系统方法的一类采样数据系统鲁棒故障检测

针对具有连续时间过程噪声和离散时间测量噪声的采样数据系统, 提出了一种新的鲁棒故障检测直接设计方法. 首先利用具有有限跳变的线性系统作为残差产生器, 采样数据系统的鲁棒故障检测设计问题被描述成采样数据滤波问题, 然后给出有限跳变线性系统有界实引理的线性矩阵不等式(LMI)表达形式, 基于此, 推导出采样数据系统鲁棒故障检测滤波器的存在条件及设计参数, 并将所提方法推广到具有结构不确定性的采样数据系统上. 所设计的滤波器能够保证残差与故障之间误差最小, 并对过程噪声、测量噪声、结构不确定性等因素鲁棒. 最后, 通过数值仿真对所提方法的可行性进行了验证.     

带有色厚尾量测噪声的鲁棒高斯近似滤波器和平滑器

为了解决带有色厚尾量测噪声的非线性状态估计问题,本文提出了新的鲁棒高斯近似（Gaussian approximate,GA）滤波器和平滑器.首先,基于状态扩展方法将量测差分后带一步延迟状态和白色厚尾量测噪声的非线性状态估计问题,转化成带厚尾量测噪声的标准非线性状态估计问题.其次,针对量测差分后模型中的噪声尺度矩阵和自由度（Degrees of freedom,DOF）参数未知问题,设计了新的高斯近似滤波器和平滑器,通过建立未知参数和待估计状态的共轭先验分布,并利用变分贝叶斯方法同时估计未知的状态、尺度矩阵、自由度参数.最后,利用目标跟踪仿真验证了本文提出的带有色厚尾量测噪声的鲁棒高斯近似滤波器和平滑器的有效性以及与现有方法相比的优越性.     

基于深度神经网络的“弱监督”密集场景人群计数算法

目前,在密集场景人群计数任务中,标注真实密度图的方法是对行人头部的中心位置进行标注,并利用高斯卷积生成真实的密度分布图作为监督信息。但是,对于密集场景而言,这样的标注方式是费时、费力的,并且密集场景图片中有诸多“非受控”因素,如低分辨率、背景噪声、目标遮挡和尺度变化等。针对这一问题,提出了一种新的标注方法,即只需要知道图片中包含多少个物体,以图片中行人的数量作为监督信息。与传统的真实密度图相比,所提出的标记方法中以真实目标的数值为“弱监督”信息。实验结果表明,对于人群回归任务,利用弱监督信息对神经网络进行训练得到的模型能够较为准确地回归出图片中所包含目标的数量,从而证明了该方法的有效性。     

基于强跟踪滤波的传感器故障诊断的改进方法

针对基于强跟踪卡尔曼滤波的传感器故障诊断方法中存在的滤波稳定性差、估计精度低的缺点,提出了双滤波器的方法。一个滤波器的量测噪声方差和系统噪声方差均大于实际值,它对故障的估计精度较低,但跟踪速度较快;另一个滤波器的算法中的量测噪声方差大于实际值,它对故障的估计精度较高,但跟踪速度较慢,正好与前者形成互补,然后用第一个滤波器实现故障的及时检测,用第二个滤波器实现对故障幅值的精确估计。仿真实验表明,该方法较好地兼顾了滤波稳定性、估计精度及速度。     

Real-time multiple people tracking using competitive condensation

The CONDENSATION (Conditional Density Propagation) algorithm has a robust tracking performance and suitability for real-time implementation. However, the CONDENSATION tracker has some difficulties with real-time implementation for multiple people tracking since it requires very complicated shape modelling and a large number of samples for precise tracking performance. Further, it shows a poor tracking performance in the case of close or partially occluded people. To overcome these difficulties, we present three improvements: First, we construct effective templates of people's shapes using the SOM (Self-Organizing Map). Second, we take the discrete HMM (Hidden Markov Modelling) for an accurate dynamical model of the people's shape transition. Third, we use the competition rule to separate close or partially occluded people effectively. Simulation results shows that the proposed CONDENSATION algorithm can achieve robust and real-time tracking in the image sequences of a crowd of people.     

一种具有在线参数调整功能的Kalman滤波及其应用

本文提出了一种具有在线调整噪声参数功能的卡尔曼自适应滤波算法及其在船舶导航目标跟踪中的应用。实际中系统噪声和量测噪声的统计特性是动态变化的,但在传统卡尔曼滤波中一般认为系统噪声模型是先验已知的,噪声均值和协方差都是定值,这必然造成滤波效果不理想、目标跟踪精度低甚至出现目标跟踪丢失的问题。针对这种情况,通过在线自适应调整噪声均值和协方差,动态跟踪噪声统计特性的变化,从而提高对目标的跟踪精度。在线实现可以有效地利用系统的部分数据进行更新迭代,减小计算量并且易于工程实现。最后通过船舶目标仿真实验的结果验证了本算法的有效性。     

集中式非线性抗差卡尔曼滤波算法研究*

针对实际中传感器的量测信息异常和传感器之间数据传输的错误时,融合系统中的数据会出现异常值(outlier)的情况,提出了一种集中式非线性卡尔曼滤波算法,该方法应用鲁棒统计理论,通过设计代价函数来对系统的量测噪声方差进行重新构造,并利用标准无味卡尔曼滤波(UKF)的观测更新算法对非线性观测方程进行滤波,该方法无需对观测方程进行线性近似,在保持鲁棒性的同时不损失UKF的滤波精度。通过一个简明实例说明了该方法在量测出现异常值的情况下依然能对目标进行有效的跟踪滤波,鲁棒性和滤波精度优于传统的Huber鲁棒跟踪方法。     

A robust optical/inertial data fusion system for motion tracking of the robot manipulator

We present an optical/inertial data fusion system for motion tracking of the robot manipulator, which is proved to be more robust and accurate than a normal optical tracking system（OTS）. By data fusion with an inertial measurement unit（IMU）, both robustness and accuracy of OTS are improved. The Kalman filter is used in data fusion. The error distribution of OTS pro-vides an important reference on the estimation of measurement noise using the Kalman filter. With a proper setup of the system and an effective method of coordinate frame synchronization, the results of experiments show a significant improvement in terms of robustness and position accuracy.     

Distributed weighted robust Kalman filter fusion for uncertain systems with autocorrelated and cross-correlated noises

In this paper, the problem of distributed weighted robust Kalman filter fusion is studied for a class of uncertain systems with autocorrelated and cross-correlated noises. The system under consideration is subject to stochastic uncertainties or multiplicative noises. The process noise is assumed to be one-step autocorrelated. For each subsystem, the measurement noise is one-step autocorrelated, and the process noise and the measurement noise are two-step cross-correlated. An optimal robust Kalman-type recursive filter is first designed for each subsystem. Then, based on the newly obtained optimal robust Kalman-type recursive filter, a distributed weighted robust Kalman filter fusion algorithm is derived for uncertain systems with multiple sensors. The distributed fusion algorithm involves a recursive computation of the filtering error cross-covariance matrix between any two subsystems. Compared with the centralized Kalman filter, the distributed weighted robust Kalman filter developed in this paper has stronger fault-tolerance ability. Simulation results are provided to demonstrate the effectiveness of the proposed approaches.     

基于强跟踪滤波器及小波变换的非线性系统参数辨识及应用

本文采用强跟踪滤波器为主要框架, 通过线性化和状态扩展解决非线性系统时变参数和状态的估计问题. 在普通强跟踪滤波器的基础上, 以小波变换估计量测噪声, 采用滤波增益调整系数解决过跟踪问题, 给出了主要的计算公式和参数的取值方法, Monte Carlo仿真和在弹道方程参数辨识中的应用结果表明, 本方法不但对突变参数具有强跟踪能力, 在噪声方差发生变化的情况下, 仍可以对非线性参数进行准确的辨识, 状态与参数估计精度高于 普通的强跟踪滤波器.     

基于抗差自适应容积卡尔曼滤波的超紧耦合跟踪方法

为降低基于单一调节回路的超紧耦合结构存在的反作用影响,设计了一种基于双回路的超紧耦合结构.基于此,为解决所设计结构中跟踪环路的非线性滤波问题,针对测量异常误差和动力学模型误差,提出了一种基于抗差自适应容积卡尔曼滤波(Robust adaptive cubature Kalman filter,RACKF)的超紧耦合跟踪算法.该算法采用稳健M估计调整容积卡尔曼滤波 (Cubature Kalman filter,CKF)算法,对观测量中粗差的影响“程度”进行探测和处理,以减小观测量异常误差产生的影响,同时利用自适应调节因子对算法进行调节修正,以处理动态扰动误差引入的影响.实验结果表明: 所提出的方法能有效地处理模型不准确所引入的误差,较好地实现全球定位系统(Global positioning system,GPS)卫星信号的高精度和稳定跟踪,其跟踪性能远优于基于单一回路的跟踪方法,同时优于基于无迹卡尔曼滤波(Unscented Kalman filter,UKF)和基于CKF的跟踪方法,提升了导航系统在高动态条件下的适应性能.     

Application of pseudolinear partitioned filter to passive vehicle tracking†

The partitioned estimation method is applied to the bearing-only target tracking problem. A pseudolinear partitioned tracking filter is developed initially in the form of recursive processing. It is then shown that such a tracking filter consists mainly of two parts, a ‘ manoeuvre predictor ’, driven by the deterministic own-sensor manoeuvre input, and a ‘ psuedolinear partitioning fixed-point smoother ’, which gives the initial position and speed estimates. Furthermore, by taking into consideration the parallel processing mechanism, a pseudolinear partitioned tracking filter with data compression is proposed to average the bearing data contaminated by the measurement noise. The parametric relationship between r.m.s. estimation error, data compressing (or renovating) interval, measurement noise level, sensor manoeuvre structure and initial range estimate is presented through Monte Carlo simulations.     

Robust nonlinear control of a three-tank system using finite-time disturbance observers

A finite-time disturbance observer-based robust control method is proposed for output tracking of the Inteco threetank system in the presence of mismatched uncertainties. The controller is designed in a backstepping manner. At each step of the virtual controller design, a robust feedback controller with some effective nonlinear damping terms is designed so that the system states remain in the feasible domain. The nonlinear uncertainty is compensated by a finite-time disturbance observer. And to avoid the shortcoming of “explosion of terms”, the dynamic surface control technique which employs a low-pass filter is adopted at each step of the virtual controller design. Attention is paid to reducing the measurement noise effects and to initialization technique of the system states and reference output trajectory. Theoretical analysis is performed to clarify the control performance. And the theoretical results are verified based on the experimental studies on the real Inteco three-tank system.     

Self-adaptive morphable model based collaborative multi-view 3d face reconstruction in visual sensor network

3D face reconstruction is an efficient method for pedestrian recognition in non-cooperative environment because of its outstanding performance in robust face recognition for uncontrolled pose and illumination changes. Visual sensor network is widely used in target surveillance as powerful unattended distributed measurement systems. This paper proposes a collaborative multi-view non-cooperative 3D face reconstruction method in visual sensor network. A peer-to-peer paradigm-based visual sensor network is employed for distributed pedestrian tracking and optimal face image acquisition. Gaussian probability distribution-based multi-view data fusion is used for target localization, and kalman filter is applied for target tracking. A lightweight face image quality evaluation method is presented to search optimal face images. A self-adaptive morphable model is designed for multiview 3D face reconstruction. To adjust the self-adaptive morphable model, the optimal face images and their poses estimation are used. Cooperative chaotic particle swarm optimization is employed for parameters optimization of the self-adaptive morphable model. Experimental results on real data show that the proposed method can acquire optimal face images and achieve non-cooperative 3D reconstruction efficiently.