Cross-spectral visual simultaneous localization and mapping (SLAM) with sensor handover

In this work, we examine the classic problem of robot navigation via visual simultaneous localization and mapping (SLAM), but introducing the concept of dual optical and thermal (cross-spectral) sensing with the addition of sensor handover from one to the other. In our approach we use a novel combination of two primary sensors: co-registered optical and thermal cameras. Mobile robot navigation is driven by two simultaneous camera images from the environment over which feature points are extracted and matched between successive frames. A bearing-only visual SLAM approach is then implemented using successive feature point observations to identify and track environment landmarks using an extended Kalman filter (EKF). Six-degree-of-freedom mobile robot and environment landmark positions are managed by the EKF approach illustrated using optical, thermal and combined optical/thermal features in addition to handover from one sensor to another. Sensor handover is primarily targeted at a continuous SLAM operation during varying illumination conditions (e.g., changing from night to day). The final methodology is tested in outdoor environments with variation in the light conditions and robot trajectories producing results that illustrate that the additional use of a thermal sensor improves the accuracy of landmark detection and that the sensor handover is viable for solving the SLAM problem using this sensor combination.     

Particle filtering with analytically guided sampling

Particle filtering (PF) is a popular nonlinear estimation technique and has been widely used in a variety of applications such as target tracking. Within the PF framework, one critical design choice is the selection of the proposal distribution from which particles are drawn. In this paper, we advocate using as proposal distribution a Gaussian-mixture-based approximation of the posterior probability density function (pdf) after taking into account the most recent measurement. The novelty of our approach is that the parameters of each Gaussian used in the mixture are determined analytically to match the modes of the underlying unknown posterior pdf. As a result, particles are sampled along the most probable regions of the state space, hence reducing the probability of particle depletion. Based on the analytically determined proposal distribution, we introduce a novel PF, termed analytically guided sampling-based PF, which is validated in range-only and bearing-only target tracking.     

基于可移动界标的微机器人的位置和角度的测量

本文提出了一种基于可移动界标的微机器人的位置和角度的测量方法。测量时在机器人上装有PSD,把三个激光装置当作界标。这种方法适合于自动运动的机器人。测量面积取决于装有激光的线性平台的长度。本文从计算和实验两个方面对于所提出的可移动界标的方法的精确性进行了评价,得出了激光装置设置的精确性影响测量精确性的结论。     

Research on tracking of maneuvering multi-target based on bionics for IRST system

This paper mainly studies bearing-only target tracking based on bionics for IRST system. Some solutions for the key problem are presented in order to apply in an actual bearing-only engineering system. They include sensor technology, measurement pretreatment technology, association gate technology, data association technology, state filtering technology, etc. The premise of these new approaches is designing an effective sensor system which can reliably search and track targets in a large range. Then, it is important to improve the confirming efficiency of the real target and limit false track overextension with the dense clutter. Then, tracking processing needs a precise target initialization information and association information between the existing target and isolated measurement. At the same time, the threat level of the bearing-only target needs to be estimated based on limited bearing-only information. Finally, aiming at unrecognized model and complex maneuvering motion for bearing-only target in polar coordinates, an effective approach of state filtering algorithm with appropriate computation cost will be given. The application of the proposed approach in an actual engineering system proves its effectiveness and practicability.     

CAD-Vision-Range-Based Self-Localization for Mobile Robot Using One Landmark

Localization is the process of determining the robot's posture within its environment including its current position and heading direction (or orientation). The process is of utmost importance for the autonomous navigational functions of a service robot. This paper describes a new localization method for service robots operating in a building based on a CAD model of the indoor environment in reasonable details. Only one specific landmark pasted within a specific region on the wall is needed. The camera with pan/tilt/zoom functions mounted on the robot first searches for this identification landmark and starts to conduct measurements using a laser rangefinder. With the polar coordinates of few measurement points on the wall and an accurate local CAD model, the exact position and orientation of the robot can be identified. This method has five distinctive advantages. First, the position of the landmark does not need to be precise. Second, each localization exercise is independent and no previous history of the moving track of the robot is required but the computational speed is still high. Third, the method is very robust with good fault-tolerance because it makes use of the reliable Hough transform. Fourth, the resolution is automatically adjusted because the panning resolution of the camera is based on the first effective measurement representing the distance of the robot from the landmark. Fifth, only the local CAD model of the room at the vicinity of the landmark needs a high precision because this model is used for localization. The system does not demand a highly accurate CAD model of the entire built environment. CAD models at other places are for navigation and path planning only.     

Visual Navigation in Natural Environments: From Range and Color Data to a Landmark-Based Model

This paper concerns the exploration of a natural environment by a mobile robot equipped with both a video color camera and a stereo-vision system. We focus on the interest of such a multi-sensory system to deal with the navigation of a robot in an a priori unknown environment, including (1) the incremental construction of a landmark-based model, and the use of these landmarks for (2) the 3-D localization of the mobile robot and for (3) a sensor-based navigation mode.For robot localization, a slow process and a fast one are simultaneously executed during the robot motions. In the modeling process (currently 0.1 Hz), the global landmark-based model is incrementally built and the robot situation can be estimated from discriminant landmarks selected amongst the detected objects in the range data. In the tracking process (currently 4 Hz), selected landmarks are tracked in the visual data; the tracking results are used to simplify the matching between landmarks in the modeling process.Finally, a sensor-based visual navigation mode, based on the same landmark selection and tracking, is also presented; in order to navigate during a long robot motion, different landmarks (targets) can be selected as a sequence of sub-goals that the robot must successively reach.     

On the behaviour of low cost laser scanners in HW/SW particle filter SLAM applications

Particle filters (PFs) are computationally intensive sequential Monte Carlo estimation methods with applications in the field of mobile robotics for performing tasks such as tracking, simultaneous localization and mapping (SLAM) and navigation, by dealing with the uncertainties and/or noise generated by the sensors as well as with the intrinsic uncertainties of the environment. However, the application of PFs with an important number of particles has traditionally been difficult to implement in real-time applications due to the huge number of operations they require. This work presents a hardware implementation on FPGA (field programmable gate arrays) of a PF applied to SLAM which aims to accelerate the execution time of the PF algorithm with moderate resource. The presented system is evaluated for different sensors including a low cost Neato XV-11 laser scanner sensor. First the system is validated by post processing data provided by a realistic simulation of a differential robot, equipped with a hacked Neato XV-11 laser scanner, that navigates in the Robot@Factory competition maze. The robot was simulated using SimTwo, which is a realistic simulation software that can support several types of robots. The simulator provides the robot ground truth, odometry and the laser scanner data. Then the proposed solution is further validated on standard laser scanner sensors in complex environments. The results achieved from this study confirmed the possible use of low cost laser scanner for different robotics applications which benefits in several aspects due to its cost and the increased speed provided by the SLAM algorithm running on FPGA.     

Simultaneous Localization of a Mobile Robot and Multiple Sound Sources Using a Microphone Array

Sound source localization is an important function in robot audition. Most existing works perform sound source localization using static microphone arrays. This work proposes a framework that simultaneously localizes the mobile robot and multiple sound sources using a microphone array on the robot. First, an eigenstructure-based generalized cross-correlation method for estimating time delays between microphones under multi-source environments is described. Using the estimated time delays, a method to compute the farfield source directions as well as the speed of sound is proposed. In addition, the correctness of the sound speed estimate is utilized to eliminate spurious sources, which greatly enhances the robustness of sound source detection. The arrival angles of the detected sound sources are used as observations in a bearing-only simultaneous localization and mapping procedure. As the source signals are not persistent and there is no identification of the signal content, data association is unknown and it is solved using the FastSLAM algorithm. The experimental results demonstrate the effectiveness of the proposed method.     

Conditions for target tracking with range-only information

This paper addresses the problem of guiding a mobile robot towards a target using only range sensors. The bearing information is not available. The target can be stationary or moving. It can be the source of some gas leakage or nuclear radiation or it can be some landmark or beacon or any manoeuvring vehicle. The mobile robot can be a ground vehicle or an aerial vehicle flying at a fixed altitude. In literature, many different strategies are proposed which use the range only measurement but they involve estimation of different parameters or have switching control strategy which make them difficult to implement. We propose two sets of conditions, one for stationary target and another for both stationary and moving target. Any control strategy, that will satisfy these conditions, can bring the robot arbitrarily close to the target. There are no restrictions on the initial conditions. Estimation of any parameter is not required. Some candidate controllers are presented that included continuous controllers and switching controllers. Simulations are carried out with these controllers to validate our result with and without measurement noise. Experimental results with ground mobile robot are presented.     

A Robust Solution to the Stereo-Vision-Based Simultaneous Localization and Mapping Problem with Steady and Moving Landmarks

The problem of visual simultaneous localization and mapping (SLAM) is examined in this paper using recently developed ideas and algorithms from modern robust control and estimation theory. A nonlinear model for a stereo-vision-based sensor is derived that leads to nonlinear measurements of the landmark coordinates along with optical flow-based measurements of the relative robot–landmark velocity. Using a novel analytical measurement transformation, the nonlinear SLAM problem is converted into the linear domain and solved using a robust linear filter. Actually, the linear filter is guaranteed stable and the SLAM state estimation error is bounded within an ellipsoidal set. A mathematically rigorous stability proof is given that holds true even when the landmarks move in accordance with an unknown control input. No similar results are available for the commonly employed extended Kalman filter, which is known to exhibit divergence and inconsistency characteristics in practice. A number of illustrative examples are given using both simulated and real vision data that further validate the proposed method.     

An approach to robot SLAM based on incremental appearance learning with omnidirectional vision

Localisation and mapping with an omnidirectional camera becomes more difficult as the landmark appearances change dramatically in the omnidirectional image. With conventional techniques, it is difficult to match the features of the landmark with the template. We present a novel robot simultaneous localisation and mapping (SLAM) algorithm with an omnidirectional camera, which uses incremental landmark appearance learning to provide posterior probability distribution for estimating the robot pose under a particle filtering framework. The major contribution of our work is to represent the posterior estimation of the robot pose by incremental probabilistic principal component analysis, which can be naturally incorporated into the particle filtering algorithm for robot SLAM. Moreover, the innovative method of this article allows the adoption of the severe distorted landmark appearances viewed with omnidirectional camera for robot SLAM. The experimental results demonstrate that the localisation error is less than 1 cm in an indoor environment using five landmarks, and the location of the landmark appearances can be estimated within 5 pixels deviation from the ground truth in the omnidirectional image at a fairly fast speed.     

Robot docking based on omnidirectional vision and reinforcement learning

We present a system for visual robotic docking using an omnidirectional camera coupled with the actor critic reinforcement learning algorithm. The system enables a PeopleBot robot to locate and approach a table so that it can pick an object from it using the pan-tilt camera mounted on the robot. We use a staged approach to solve this problem as there are distinct subtasks and different sensors used. Starting with random wandering of the robot until the table is located via a landmark, then a network trained via reinforcement allows the robot to turn to and approach the table. Once at the table the robot is to pick the object from it. We argue that our approach has a lot of potential allowing the learning of robot control for navigation and remove the need for internal maps of the environment. This is achieved by allowing the robot to learn couplings between motor actions and the position of a landmark.     

An enhanced topological map for efficient and reliable mobile robot navigation with imprecise sensors

An enhanced topological mapping system for efficient and reliable navigation is presented. The map has a topological framework and some additional features. Firstly, it utilizes such rough metrical information as the length and orientation of the links. Secondly, it provides a reliable localization algorithm with which the robot first finds the interval describing the robot’s probable location by estimating the projected traveled distance using dead reckoning and then fine-tunes the estimation using landmark detection modules. Finally, it provides a planning algorithm with which the robot’s path is chosen so that the robot reaches the goal location as fast as possible without losing its way despite using such imprecise sensors as ultrasonic range finders.We have implemented and tested the proposed mapping system both on a simulator and a real mobile robot, the CAIR-2. This paper also describes landmark detection modules that utilize ultrasonic range finders. Although landmark detection modules are too simple and imprecise to estimate position by themselves, these experiments show that the proposed mapping system can reliably guide robots.     

Terrain-Based Navigation for Underwater Vehicles Using an Ultrasonic Scanning System

In this paper an approach to the field of outdoor robotic navigation with a focus on underwater simultaneous localization and mapping (SLAM) is proposed that utilizes ultrasonic scanning images. Experimental results from the implementation of a SLAM algorithm with real data are presented. The projected landmark detection process constructs a map of the environment and generates navigation estimates based on an adaptive delayed nearest-neighbor algorithm. The feature extraction and validation processes are resolved at the sensor level using a simple local maximum-level detection algorithm on the range data. This paper presents experimental results from our research efforts in the above area, using data from water tank trials and a remotely operated vehicle operating in a shallow water environment.     

基于EKF的全景视觉机器人SLAM算法

研究全景视觉机器人同时定位和地图创建（SLAM）问题。针对普通视觉视野狭窄, 对路标的连续跟踪和定位能力差的问题, 提出了一种基于改进的扩展卡尔曼滤波（EKF）算法的全景视觉机器人SLAM方法, 用全景视觉得到机器人周围的环境信息, 然后从这些信息中提取出环境特征, 定位出路标位置, 进而通过EKF算法同步更新机器人位姿和地图库。仿真实验和实体机器人实验结果验证了该算法的准确性和有效性, 且全景视觉比普通视觉定位精度更高。     

Saliency and spatial information-based landmark selection for mobile robot navigation in natural environments

In this paper, a landmark selection and tracking approach is presented for mobile robot navigation in natural environments, using textural distinctiveness-based saliency detection and spatial information acquired from stereo data. The presented method focuses on achieving high robustness of tracking rather than self-positioning accuracy. The landmark selection method is designed to select a small amount of the most salient feature points in a wide variety of sparse unknown environments to ensure successful matching. Landmarks are selected by an iterative algorithm from a textural distinctiveness-based saliency map extended with spatial information, where a repulsive potential field is created around the position of each already selected landmark for better distribution in order to increase robustness. The template matching of landmarks is aided with visual odometry-based motion estimation. Other robustness increasing strategies includes estimating landmark positions by unscented Kalman filters as well as from surrounding landmarks. Experimental results show that the introduced method is robust and suitable for natural environments.     

A New Sonar-based Landmark for Localization in Indoor Environments

This paper presents a new sonar based landmark to represent significant places in an environment for localization purposes. This landmark is based on extracting the contour free of obstacles around the robot from a local evidence grid. This contour is represented by its curvature, calculated by a noise-resistant function which adapts to the natural scale of the contour at each point. Then, curvature is reduced to a short feature vector by using Principal Component Analysis. The landmark calculation method has been successfully tested in a medium scale real environment using a Pioneer robot with Polaroid sonar sensors.     

Selecting Landmarks for Localization in Natural Terrain

We describe techniques to optimally select landmarks for performing mobile robot localization by matching terrain maps. The method is based upon a maximum-likelihood robot localization algorithm that efficiently searches the space of possible robot positions. We use a sensor error model to estimate a probability distribution over the terrain expected to be seen from the current robot position. The estimated distribution is compared to a previously generated map of the terrain and the optimal landmark is selected by minimizing the predicted uncertainty in the localization. This approach has been applied to the generation of a sensor uncertainty field that can be used to plan a robot's movements. Experiments indicate that landmark selection improves not only the localization uncertainty, but also the likelihood of success. Examples of landmark selection are given using real and synthetic data.     

变周期RP-EKF时延纯方位角目标跟踪算法

针对声传感器纯方位目标跟踪应用,提出了一种基于静态多模型的距离参数化变周期扩展卡尔曼滤波算法,将时延影响转换为模型的可变周期,并通过参数在线估计的方法,估计该可变周期.距离参数化方法将整个距离空间分成若干个区间,分区间对距离进行估计,进而搜索出目标与观测平台之间的正确距离值.仿真结果表明变周期距离参数化扩展卡尔曼滤波算法能有效解决经典扩展卡尔曼滤波算法在纯方位角目标跟踪时可能出现的滤波发散现象,并能处理声音信号的传输时间延迟问题.