Markov Localization Research Articles

Compressed Sensing-Based Genetic Markov Localization for Mobile Transmitters

With the strengths of quickness, low cost, and adaptability, unmanned aerial vehicle (UAV) communication is widely utilized in the next-generation wireless network. However, some risks and hidden dangers such as UAV “black flight” disturbances, attacks, and spying incidents lead to the necessity of the real-time supervision of UAVs. A compressed sensing-based genetic Markov localization method is proposed in this paper for two-dimensional trajectory tracking of the mobile transmitter in a finite domain, which consists of three modules: the multi-station sampling module, the reconstruction module, and the localization module. In the multi-station sampling module, multiple stations are deployed to receive the signal transmitted by the UAV using compressed sensing, and the motion model of the mobile transmitter is the constant turn rate and acceleration (CTRA) model. In the reconstruction module, we propose a direct reconstruction method to extract the joint cross-spatial spectrum. In the genetic Markov localization module, we propose a two-step localization method to genetically correct the inaccurate points in the preliminary results and generate the tracking result. Extensive simulations are conducted to verify the effectiveness of the proposed method. The results show that the proposed method is superior to the particle filter method and the Markov Monte Carlo method at all sampling moments. Specifically, when SNR = 15dB, the root-mean-square error (RMSE) of the proposed method is 39% and 60% lower than that of the other two methods, respectively. Moreover, under the premise that the RMSE of the localization result is less than 30 m, the reconstruction module can reduce the running time of the proposed method by 33.3%.

Pursuer Assignment and Control Strategies in Multi-Agent Pursuit-Evasion Under Uncertainties.

We consider a pursuit-evasion problem with a heterogeneous team of multiple pursuers and multiple evaders. Although both the pursuers and the evaders are aware of each others’ control and assignment strategies, they do not have exact information about the other type of agents’ location or action. Using only noisy on-board sensors the pursuers (or evaders) make probabilistic estimation of positions of the evaders (or pursuers). Each type of agent use Markov localization to update the probability distribution of the other type. A search-based control strategy is developed for the pursuers that intrinsically takes the probability distribution of the evaders into account. Pursuers are assigned using an assignment algorithm that takes redundancy (i.e., an excess in the number of pursuers than the number of evaders) into account, such that the total or maximum estimated time to capture the evaders is minimized. In this respect we assume the pursuers to have clear advantage over the evaders. However, the objective of this work is to use assignment strategies that minimize the capture time. This assignment strategy is based on a modified Hungarian algorithm as well as a novel algorithm for determining assignment of redundant pursuers. The evaders, in order to effectively avoid the pursuers, predict the assignment based on their probabilistic knowledge of the pursuers and use a control strategy to actively move away from those pursues. Our experimental evaluation shows that the redundant assignment algorithm performs better than an alternative nearest-neighbor based assignment algorithm1.

Image-Based Geo-Localization Using Satellite Imagery

The problem of localization on a geo-referenced satellite map given a query ground view image is useful yet remains challenging due to the drastic change in viewpoint. To this end, in this paper we work on the extension of our earlier work on the Cross-View Matching Network (CVM-Net) (Hu et al. in IEEE conference on computer vision and pattern recognition (CVPR), 2018) for the ground-to-aerial image matching task since the traditional image descriptors fail due to the drastic viewpoint change. In particular, we show more extensive experimental results and analyses of the network architecture on our CVM-Net. Furthermore, we propose a Markov localization framework that enforces the temporal consistency between image frames to enhance the geo-localization results in the case where a video stream of ground view images is available. Experimental results show that our proposed Markov localization framework can continuously localize the vehicle within a small error on our Singapore dataset.

Indoor mobile robot localization in dynamic and cluttered environments using artificial landmarks

Purpose Robot localization in dynamic, cluttered environments is a challenging problem because it is impractical to have enough knowledge to be able to accurately model the robot’s environment in such a manner. This study aims to develop a novel probabilistic method equipped with function approximation techniques which is able to appropriately model the data distribution in Markov localization by using the maximum statistical power, thereby making a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environments. Design/methodology/approach The parameter vector of the statistical model is in the form of positions of easily detectable artificial landmarks in omnidirectional images. First, using probabilistic principal component analysis, the most likely set of parameters of the environmental model are extracted from the sensor data set consisting of missing values. Next, we use these parameters to approximate a probability density function, using support vector regression that is able to calculate the robot’s pose vector in each state of the Markov localization. At the end, using this density function, a good approximation of conditional density associated with the observation model is made which leads to a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environment. Findings The authors validate their method in an indoor office environment with 34 unique artificial landmarks. Further, they show that the accuracy remains high, even when they significantly increase the dynamics of the environment. They also show that compared to those appearance-based localization methods that rely on image pixels, the proposed localization strategy is superior in terms of accuracy and speed of convergence to a global minima. Originality/value By using easily detectable, and rotation, scale invariant artificial landmarks and the maximum statistical power which is provided through the concept of missing data, the authors have succeeded in determining precise pose updates without requiring too many computational resources to analyze the omnidirectional images. In addition, the proposed approach significantly reduces the risk of getting stuck in a local minimum by eliminating the possibility of having similar states.

SeqSLAM++: View-based robot localization and navigation

This paper presents a new approach to view-based localization and navigation in outdoor environments, which are indispensable functions for mobile robots. Several approaches have been proposed for autonomous navigation. GPS-based systems are widely used especially in the case of automobiles, however, they can be unreliable or non-operational near tall buildings. Localization with a precise 3D digital map of the target environment also enables mobile robots equipped with range sensors to estimate accurate poses, but maintaining a large-scale outdoor map is often costly. We have therefore developed a novel view-based localization method SeqSLAM++ by extending the conventional SeqSLAM in order not only to robustly estimate the robot position comparing image sequences but also to cope with changes in a robot’s heading and speed as well as view changes using wide-angle images and a Markov localization scheme. According to the direction to move provided by the SeqSLAM++, the local-level path planner navigates the robot by setting subgoals repeatedly considering the structure of the surrounding environment using a 3D LiDAR. The entire navigation system has been implemented in the ROS framework, and the effectiveness and accuracy of the proposed method was evaluated through off-line/on-line navigation experiments.

HyRise

Floor localization is an integral part of indoor localization systems that are deployed in any typical high-rise building. Nevertheless, while many efforts have been made to detect floor change events leveraging phone-embedded sensors, there are still a number of pitfalls that need to be overcome to provide robust and accurate localization in the 3D space. In this paper, we present HyRise: a robust and ubiquitous probabilistic crowdsourcing-based floor determination system. HyRise is a hybrid system that combines the barometer sensor and the ubiquitous Wi-Fi access points installed in the building into a probabilistic framework to identify the user's floor. In particular, HyRise incorporates a discrete Markov localization algorithm where the motion model is based on the vertical transitions detected from the sampled pressure readings and the observation model is based on the overheard Wi-Fi access points (APs) to find the most probable floor of the user. HyRise also has provisions to handle practical deployment issues including handling the inherent drift in the barometer readings, the noisy wireless environment, heterogeneous devices, among others. HyRise is implemented on Android phones and evaluated using three different testbeds: a campus building, a shopping mall, and a residential building with different floorplan layouts and APs densities. The results show that HyRise can identify the exact user's floor correctly in 93%, 92% and 77% of the cases for the campus building, the shopping mall, and the more challenging residential building; respectively. In addition, it can identify the floor with at most 1-floor error in 100% of the cases for all three testbeds. Moreover, the floor localization accuracy outperforms that achieved by other state-of-the-art techniques by at least 79% and up to 278%. This accuracy is achieved with no training overhead, is robust to the different user devices, and is consistent in buildings with different structures and APs densities.

Vision-based Markov localization for long-term autonomy

Lifelong autonomous operation has gained much attention in the field of mobile robotics in recent years. In the context of robot navigation based on vision, lifelong applications include scenarios with substantial perceptual changes due to changes in season, illumination and weather. In this paper, we present an approach to localize a mobile robot, equipped with a low frequency camera, with respect to an image sequence recorded in a different season. Our approach employs a discrete Bayes filter with a sensor model based on whole image descriptors. We compute a similarity matrix over all image descriptors and leverage the sequential nature of typical image streams with a flexible transition scheme in the Bayes filter framework. Since we compute a probability distribution over the entire state space, our approach can handle complex trajectories that may include same season loop-closures as well as fragmented sub-sequences. Furthermore, we show that decorrelating the similarity matrix results in an improved localization performance. Through an extensive experimental evaluation on challenging datasets we demonstrate that our approach outperforms state-of-the-art techniques.

Episodic non-Markov localization

Markov localization and its variants are widely used for mobile robot localization. These methods assume Markov independence of observations, implying that the observations can be entirely explained by a map. However, in real human environments, robots frequently make unexpected observations due to unmapped static objects like chairs and tables, and dynamic objects like humans. We therefore introduce Episodic non-Markov Localization (EnML), which reasons about the world as consisting of three classes of objects: long-term features corresponding to permanent mapped objects, short-term features corresponding to unmapped static objects, and dynamic features corresponding to unmapped moving objects. Long-term features are represented by a static map, while short-term features are detected and tracked in real-time. To reason about unexpected observations and their correlations across poses, we augment the Dynamic Bayesian Network for Markov localization to include varying edges and nodes, resulting in a novel Varying Graphical Network representation. The maximum likelihood estimate of the belief is incrementally computed by non-linear functional optimization. By detecting timesteps along the robot’s trajectory where unmapped observations prior to such time steps are unrelated to those afterwards, EnML limits the history of observations and pose estimates to “episodes” over which the belief is computed. We demonstrate EnML using different types of sensors including laser rangefinders and depth cameras, and over multiple datasets, comparing it with alternative approaches. We further include results of a team of indoor autonomous service mobile robots traversing hundreds of kilometers using EnML.

Topological Indoor Localization and Navigation for Autonomous Mobile Robot

Mobile robot typically has limited on-board resources and may be applied in different indoor environment. Thus, it is necessary that they can learn a map and navigate themselves autonomously with lightweight algorithms. A novel topological map-building-based localization and navigation method is proposed in this paper. Based on the depth curve provided by a 3D sensor, a progressive Bayesian classifier is developed to realize direct corridor type identification. Instead of extracting features from single observation, information from multi-observations are fused to achieve a more robust performance. A topological map generation and loop closing method are proposed to build the environment map through autonomous exploration. Based on the derived map and the Markov localization method, the robot can then localize itself and navigate freely in the indoor environment. Experiments are performed on a recently built mobile robot system, and the results verify the effectiveness of the proposed methodology.

Probabilistic Approach to Mobile Robot Localization Based on Gaussian Models of Sensors

In this paper the probabilistic approach to mobile robot localization is discussed. Generally probabilistic localization uses some type of sensors model. In this paper Gaussian model, which is the most appropriate probabilistic model of the sensors, is used. The main body of the article deal with the proposal of own approach to probabilistic localization, which is inspired by Markov localization. That is why the Markov localization is described in the introduction of the article. At the end of the article several experiments with the real robot are described. Results of the experiments have proven that proposed localization is accurate, fast and reliable.

1P1-D06 全方位カメラを持つ屋外移動ロボットの見えに基づく誘導(移動ロボットのための視覚)

Autonomous navigation in outdoor environments requires self-localization capability. This report describes an application of a view-based localization method to mobile robot navigation. Our localization method uses a two-stage SVM-based localization for increasing robustness to change of weather and seasons. It also adopts a Markov localization approach for an efficient and reliable localization. In applying the view-based method to mobile robot navigation, the change of robot heading during motion may degrade the localization performance. To cope with this problem, we use a panoramic image to search a range of orientations for a matched view. Additionally, a navigation course is divided into straight courses by corner. We also deal with a corner localization by examining views corresponding to the heading before and after turning the corner. We validated the effectiveness of the method using actual image sequences.

Hybrid Map-Based Navigation Method for Unmanned Ground Vehicle in Urban Scenario

To reduce the data size of metric map and map matching computational cost in unmanned ground vehicle self-driving navigation in urban scenarios, a metric-topological hybrid map navigation system is proposed in this paper. According to the different positioning accuracy requirements, urban areas are divided into strong constraint (SC) areas, such as roads with lanes, and loose constraint (LC) areas, such as intersections and open areas. As direction of the self-driving vehicle is provided by traffic lanes and global waypoints in the road network, a simple topological map is fit for the navigation in the SC areas. While in the LC areas, the navigation of the self-driving vehicle mainly relies on the positioning information. Simultaneous localization and mapping technology is used to provide a detailed metric map in the LC areas, and a window constraint Markov localization algorithm is introduced to achieve accurate position using laser scanner. Furthermore, the real-time performance of the Markov algorithm is enhanced by using a constraint window to restrict the size of the state space. By registering the metric maps into the road network, a hybrid map of the urban scenario can be constructed. Real unmanned vehicle mapping and navigation tests demonstrated the capabilities of the proposed method.

실외 이동로봇의 고도지도 기반의 전역 위치추정을 위한 Hausdorff 거리 정합 기법

Mobile robot localization is the task of estimating the robot pose in a given environment. This research deals with outdoor localization based on an elevation map. Since outdoor environments are large and contain many complex objects, it is difficult to robustly estimate the robot pose. This paper proposes a Hausdorff distance-based map matching method. The Hausdorff distance is exploited to measure the similarity between extracted features obtained from the robot and elevation map. The experiments and simulations show that the proposed Hausdorff distance-based map matching is useful for robust outdoor localization using an elevation map. Also, it can be easily applied to other probabilistic approaches such as a Markov localization method.

Comparison of Distance Measurement Between Stereo Vision and Ultrasonic Sensor

Problem statement: The traditional approaches in mobile robot research are the use of stereo vision systems to extract range information from pairs of images. One of the difficulties of techniques in navigation is that the intrinsic computational expense of extracting three dimensional information from stereo pairs of images. Approach: To compare performance sensor readings from environment between stereo vision and ultra sonic sensor. Results: The robot was tested by experiment that it runs with Markov localization algorithm in distance 5 m and records the robot’s distance information from stereo vision system and ultrasonic sensor. Conclusion: This study mainly described and compared distance measurement by stereo vision and ultrasonic sensor.

Cooperative Distributed Sensors for Mobile Robot Localization

This paper presents a probabilistic algorithm to collaborate distributed sensors for mobile robot localization. It uses a sample-based version of Markov localization—Monte Carlo localization (MCL), capable of localizing mobile robot in an any-time fashion. During robot localization given a known environment model, MCL method is employed to update robot’s belief whichever information (positive or negative) attained from environmental sensors. Meanwhile, an implementation is presented that uses color environmental cameras for robot detection. All the parameters of each environmental camera are unknown in advance and need be calibrated independently by robot. Once calibrated, the positive and negative detection models can be built up according to the parameters of environmental cameras. A further experiment, obtained with the real robot in an indoor office environment, illustrates it has drastic improvement in global localization speed and accuracy using our algorithm.

실외 이동로봇의 고도지도 기반 위치인식을 위한 고도관성모멘트 추출 및 정합

The problem of outdoor localization can be practically solved by GPS. However, GPS is not perfect and some areas of outdoor navigation should consider other solutions. This research deals with outdoor localization using an elevation map without GPS. This paper proposes a novel feature, elevation moment of inertia (EMOI), which represents the distribution of elevation as a function of distance from a robot in the elevation map. Each cell of an elevation map has its own EMOI, and outdoor localization can be performed by matching EMOIs obtained from the robot and the pre-given elevation map. The experiments and simulations show that the proposed EMOI can be usefully exploited for outdoor localization with an elevation map and this feature can be easily applied to other probabilistic approaches such as Markov localization method.

A Probabilistic Approach to Tracking Moving Targets With Distributed Sensors

Tracking a moving target is a difficult task in a distributed sensor network due to the lack of knowledge of the target's motion and signal noises. Several existing approaches use only sensory information or may require accurate target's motion models. In this paper, we present a Markovian approach that combines dynamically estimated target's motion models with received sensory information. The approach localizes a target by using the estimated motion models and the provided sensory model. We characterize probabilistic conditions under which the estimation accuracy increases if more sensors are used, and the estimations converge to the target's real position asymptotically. Our experimental analysis shows that our approach leads to substantially more accurate and robust location estimations than the previous approaches using only sensory information, and it is competitive with the standard Markov localization approach.

Appearance-based localization for mobile robots using digital zoom and visual compass

This paper describes a localization system for mobile robots moving in dynamic indoor environments, which uses probabilistic integration of visual appearance and odometry information. The approach is based on a novel image matching algorithm for appearance-based place recognition that integrates digital zooming, to extend the area of application, and a visual compass. Ambiguous information used for recognizing places is resolved with multiple hypothesis tracking and a selection procedure inspired by Markov localization. This enables the system to deal with perceptual aliasing or absence of reliable sensor data. It has been implemented on a robot operating in an office scenario and the robustness of the approach is demonstrated experimentally.

The Reverse Monte Carlo localization algorithm

Global localization is a very fundamental and challenging problem in Robotic Soccer. Here, the main aim is to find the best method which is very robust and fast and requires less computational resources and memory compared to similar approaches and is precise enough for robot soccer games and technical challenges. In this work, the Reverse Monte Carlo localization (R-MCL) method is introduced. The algorithm is designed for fast, precise and robust global localization of autonomous robots in the robotic soccer domain, to overcome the uncertainties in the sensors, environment and the motion model. R-MCL is a hybrid method based on Markov localization (ML) and Monte Carlo localization (MCL), where the ML based module finds the region where the robot should be and the MCL based part predicts the geometrical location with high precision by selecting samples in this region. It is called Reverse since the MCL routine is applied in a reverse manner in this algorithm. In this work, this method is tested on a challenging data set that is used by many other researchers and compared in terms of error rate against different levels of noise, and sparsity. Additionally, the time required to recover from kidnapping and the processing time of the methods are tested and compared. According to the test results R-MCL is a considerable method against high sparsity and noise. It is preferable when its recovery from kidnapping and processing times are considered. It gives robust and fast but relatively coarse position estimations against imprecise and inadequate perceptions, and coarse action data, including regular misplacements, and false perceptions.

Comparison of Localization Methods for a Robot Soccer Team

In this work, several localization algorithms that are designed and implemented for Cerberus'05 Robot Soccer Team are analyzed and compared. These algorithms are used for global localization of autonomous mobile agents in the robotic soccer domain, to overcome the uncertainty in the sensors, environment and the motion model. The algorithms are Reverse Monte Carlo Localization (R-MCL), Simple Localization (S-Loc) and Sensor Resetting Localization (SRL). R-MCL is a hybrid method based on both Markov Localization (ML) and Monte Carlo Localization (MCL) where the ML module finds the region where the robot should be and MCL predicts the geometrical location with high precision by selecting samples in this region. S-Loc is another localization method where just one sample per percept is drawn, for global localization. Within this method another novel method My Environment (ME) is designed to hold the history and overcome the lack of information due to the drastically decrease in the number of samples in S-Loc. ME together with S-Loc is used in the Technical Challenges in Robocup 2005 and play an important role in ranking the First Place in the Challenges. In this work, these methods together with SRL, which is a widely used successful localization algorithm, are tested with both offline and real-time tests. First they are tested on a challenging data set that is used by many researches and compared in terms of error rate against different levels of noise, and sparsity. Besides time required recovering from kidnapping and speed of the methods are tested and compared. Then their performances are tested with real-time tests with scenarios like the ones in the Technical Challenges in ROBOCUP. The main aim is to find the best method which is very robust and fast and requires less computational power and memory compared to similar approaches and is accurate enough for high level decision making which is vital for robot soccer.