Active particle in MCL: An evolutionary view

Mobile robot localization is a very important problem in robotics as most robot's tasks need the positional information. Monte Carlo Localization(MCL) is one of the most popular and efficient localization algorithms for mobile robot localization. MCL algorithm represents a robot's pose by a set of weighted particles. In order to further improve the performance of MCL, many extensions have been proposed. In this paper, we proposed an algorithm called dynamic size MCL, an extension of MCL. We incorporate the clustering approach into traditional MCL. With the help of clustering information, our algorithm could reduce the number of particles during the process of localization, which lower the computational cost. Experimental results demonstrate the effectiveness of the proposed method.

Robust Monte Carlo localization for mobile robots

For localizing workers and equipments in readiness sap, based on Monte Carlo Localization (MCL) algorithm, an accurate localization algorithm for mobile sensor network, in which anchor nodes are fixed and normal nodes can move freely, is presented in this paper. Adopting two dimensional Newton interpolation to predict the movement path of normal node, localization selection model is builded, and by designing an adaptive method of computing the radio range of node, the impact of unsteady radio range to prediction result can be improved. Result of simulation indicates that the accuracy of new localization algorithm is higher, and the request of anchor node density is much lower than the basic MCL algorithm.

An improved 2D weighted centroid localization algorithm for Wireless Sensor Networks (WSNs) is proposed. Compared with most previous approaches, the improved algorithm uses a optimized threshold to restrict the number of anchors involved. Simulation results show that our improved weighted centroid localization algorithm increased the positioning accuracy by at least 15% than traditional Monte Carlo Localization (MCL) algorithm. Furthermore, the algorithm is extended from 2D to 3D and comparison with MCL method both in accuracy and in efficiency are carried out as well, showing that 3D-WCL algorithm has a high accuracy about 13% localization average error significantly with little hardware cost, which is suitable for practical application, such as medical surveillance in hospital etc.

Reliability is a key factor for realizing safety guarantee of fully autonomous robot systems. In this paper, we focus on reliability in mobile robot localization. Monte Carlo localization (MCL) is widely used for mobile robot localization. However, it is still difficult to guarantee its safety because there are no methods determining reliability for MCL estimate. This paper presents a novel localization framework that enables robust localization, reliability estimation, and quick relocalization, simultaneously. The presented method can be implemented using a similar estimation manner to that of MCL. The method can increase localization robustness to environment changes by estimating known and unknown obstacles while performing localization; however, localization failure of course occurs by unanticipated errors. The method also includes a reliability estimation function that enables a robot to know whether localization has failed. Additionally, the method can seamlessly integrate a global localization method via importance sampling. Consequently, quick relocalization from a failure state can be realized while mitigating noisy influence of global localization. We conduct three types of experiments using wheeled mobile robots equipped with a two‐dimensional LiDAR. Results show that reliable MCL that performs robust localization, self‐failure detection, and quick failure recovery can be realized.

Self-localization, a basic problem in mobile robot systems, can be divided into two subproblems: pose tracking and global localization. In pose tracking, the initial robot pose is known, and localization seeks to identify small, incremental errors in a robot’s odometry (Leonard & Durrant-Whyte, 1991). In global localization, however the robot is required to estimate its pose by local and incomplete observed information under the condition of uncertain initial pose. Global localization is a more challenging problem. Only most recently, several approaches based on probabilistic theory are proposed for global localization, including grid-based approaches (Burgard et al., 1996), topological approaches (Kaelbling et al., 1996) (Simmons & Koenig, 1995), Monte Carlo localization (Dellaert et al., 1999) and multi-hypothesis tracking (Jensfelt & Kristensen, 2001) (Roumeliotis & Bekey, 2000). By representing probability densities with sets of samples and using the sequential Monte Carlo importance sampling (Andrieu & Doucet, 2002), Monte Carlo localization (MCL) can represent non-linear and non-Gaussian models well and focus the computational resources on regions with high likelihood. So MCL has attracted wide attention and has been applied in many real robot systems. But traditional MCL has some shortcomings. Since samples are actually drawn from a proposal density, if the observation density moves into one of the tails of the proposal density, most of the samples’ non-normalized importance factors will be small. In this case, a large sample size is needed to represent the true posterior density to ensure stable and precise localization. Another problem is that samples often too quickly converge to a single, high likelihood pose. This might be undesirable in the case of localization in symmetric environments, where multiple distinct hypotheses have to be tracked for extended periods of time. How to get higher localization precision, to improve efficiency and to prevent premature convergence of MCL are the key concerns of the researchers. To make the samples represent the posterior density better, Thrun et al. proposed mixtureMCL (Thrun et al., 2001), but it needs much additional computation in the sampling process. To improve the efficiency of MCL, methods adjusting sample size adaptively over time are proposed (Fox, 2003) (Koller & Fratkina, 1998), but they increase the probability of premature convergence. Although clustered particle filters are applied to solve premature convergence (Milstein et al., 2002), the method loses the advantage of focusing the computational resources on regions with high likelihood because it maintains the same sample size for all clusters. In this paper, a new version of MCL is proposed to overcome those limitations. Samples are clustered into groups which are also called species. A coevolutionary model derived from competition of ecological species is introduced to

Vehicle localization is one of the key technical factors for autonomous vehicles. It requires high accuracy, precision, and robustness towards various road conditions. Popular localization methods include global navigation satellite system (GNSS) and visual methods, but their accuracy can degrade in some conditions. This work proposes to use the environmental magnetic field (EMF) for localization to complement the shortcomings of existing methods. EMF is a combination of the Earth’s geomagnetic field and magnetic field induced by man-made objects. It has local fluctuations that can be paired with coordinate positions and is time-invariant within a practical timescale. Past works considering its use in road vehicles had problems when applying them to the localization of autonomous vehicles. This work overcomes the problems by creating a two-dimensional magnetic field map using Gaussian process regression, using magnetic markers to enhance EMF fluctuations, and utilizing the Monte Carlo localization (MCL) algorithm. In this work, a 2-D EMF map was generated using measurements from our experiment vehicles. The generated map retained the original magnetic features. Next, the proposed method was validated through simulation and actual vehicle tests using the generated EMF map as a reference. The results showed that the MCL can accurately localize the vehicle and demonstrated the feasibility of the proposed method.

Mobile robots, in modern technology, demand a more robust localization in a complex environment. Currently, the most commonly used 2D LiDAR localization system for mobile robots requires maps that are constructed by 2D SLAM. Such systems do not cope well with dynamic environments and also have high deployment costs when moving robots to a new environment setting as they require the reconstruction of a map for each new place. In modern days, a floor plan is indispensable for an indoor environment. It typically represents essential structures such as walls, corners, pillars, etc. for humans to navigate in the environment. This information turns out to be crucial for robot localization. In this paper, we propose an approach for 2D LiDAR localization in an architectural floor plan. We use partial simultaneous localization and mapping (PSLAM) algorithm to generate a map while we concurrently aligned it to the floor plan using Monte Carlo Localization (MCL) method. Real-world experiments have been conducted with our proposed method which results in robust robot localization, the algorithm is even evaluated on a large discrepancies floor plan (discrepancies between the floor plan and real-world). Our algorithm demonstrates that its capabilities of localizing in real-time applications.

The combined action of several sensing systems, so that they are able to compensate the technical flaws of each other, is common in robotics. Monte Carlo Localization (MCL) is a popular technique used to estimate the pose of a mobile robot, which allows the fusion of heterogeneous sensor data. Several sensor fusion schemes have been proposed which include sensors like GPS to improve the performance of this algorithm. In this paper, an Adaptive MCL algorithm is used to combine data from wheel odometry, an inertial measurement unit, a global positioning system and laser scanning. A particle weighting model which integrates GPS measurements is proposed, and its performance is compared with a particle generation approach. Experiments were conducted on a real robotic car within an urban environment.

As a probabilistic localization algorithm, Monte Carlo localization (MCL) method has been widely used for mobile robot localization over the past decade. In this paper, an extended MCL method (EMCL) is developed by incorporating two different resampling processes, namely importance resampling and sensor-based resampling, to conventional MCL for improvement of localization performance. Different resampling processes are utilized based on a matching of sample distribution and observations. Two additional processes for validating over-convergence and uniformity are introduced for examination of such matching. A visual based EMCL is further implemented using a triangulation-based resampling from visual features recognized by Bayesian networks. Experiments are conducted to demonstrate the validity of the proposed approach

Global localization and kidnapping are two challenging problems in robot localization. The popular method, Monte Carlo Localization (MCL) addresses the problem by iteratively updating a set of particles with a “sampling-weighting” loop. Sampling is decisive to the performance of MCL [1]. However, traditional MCL can only sample from a uniform distribution over the state space. Although variants of MCL propose different sampling models, they fail to provide an accurate distribution or generalize across scenes. To better deal with these problems, we present a distribution proposal model named Deep Samplable Observation Model (DSOM). DSOM takes a map and a 2D laser scan as inputs and outputs a conditional multimodal probability distribution of the pose, making the samples more focusing on the regions with higher likelihood. With such samples, the convergence is expected to be more effective and efficient. Considering that the learning-based sampling model may fail to capture the accurate pose sometimes, we furthermore propose the Adaptive Mixture MCL (AdaM MCL), which deploys a trusty mechanism to adaptively select updating mode for each particle to tolerate this situation. Equipped with DSOM, AdaM MCL can achieve more accurate estimation, faster convergence and better scalability than previous methods in both synthetic and real scenes. Even in real environments with long-term changes, AdaM MCL is able to localize the robot using DSOM trained only by simulation observations from a SLAM map or a blueprint map. Source code for this paper is available here: https://github.com/Runjian-Chen/AdaM_MCL.

With the development of electronic technology and communication protocols, wireless sensor network technology is developing rapidly. In a sense, the traditional static wireless sensor network has been unable to meet the needs of new applications. However, the introduction of mobile nodes extends the application of wireless sensor networks, despite the technical challenges. Because of its flexibility, the mobile wireless sensor network has attracted great attention, and even small, self-controlled mobile sensor devices have appeared. At present, mobile node localization has become one of the hotspots in wireless sensor networks. As the storage energy of wireless sensor network nodes is limited, and the communication radius is small, many scientists have focused their research direction on the location algorithm of mobile nodes. According to the continuity principle of mobile node movement, in this paper we propose an improved mobile node localization algorithm based on the Monte Carlo Location (MCL) algorithm, and the method can reduce the sampling interval effectively. First of all, this paper introduces the structure and classification of wireless sensor localization technology. Secondly, the principle of the Monte Carlo Location algorithm is described in detail. Thirdly, we propose an efficient method for mobile node localization based on the MCL algorithm. Finally, the effectiveness and accuracy of the new algorithm are verified by comparative analysis.

This paper proposes an adaptive Monte Carlo location (MCL) algorithm in stages to improve the common problems existed in the traditional MCL method, such as the high computational complexity, and the hijacked circumstance for the mobile robot. Firstly, the current positioned state, namely global localization or local localization, is judged. Secondly, different particles are assigned to conduct localization and tracking based on the different status of positioning allocation. In addition, the accuracy of the positioning system is used to detect whether the robot has been hijacked or not. The research of SLAM carried out on the Robot Operating System (ROS) shows that the proposed method is effective in achieving an accurate localization and reducing the computational complexity. It is suitable for indoor mobile robot SLAM.

In this paper, we focus on configuring a low cost RFID (Radio Frequency IDentification) system with less RFID readers and low density RFID tag textiles for stable and accurate self-localization for an omni-directional mobile robot. An RFID system using multiple RFID readers and high density RFID tags has already been applied to an indoor mobile robot self-localization. However, the system production cost is relatively high. To reduce the production cost of the system while maintaining the self-localization performance, we redesign two new RFID systems configured with 20 RFID readers and 5 RFID readers enlarging the size of an RFID reader antenna. Particularly, a new likelihood function of the tag detection with the redesigned large size RFID reader antenna is proposed for Monte Carlo Localization (MCL). Furthermore, we come up with a novel arrangement of RFID tags installed in a hexagon pattern. The new arrangement of ID tags enables both low cost ID tag installation and high performance of self-localization. We examine the performances of robot self-localization with less RFID readers and lower density RFID tags installed on the floor based on MCL(Monte Carlo localization). The simulation demonstrates the validity of our proposed configuration of the RFID based self-localization.

The elevation map is one of the most popular maps for outdoor navigation. We propose the elevation moment of inertia (EMOI), which represents the distribution of elevation around a robot in an elevation map, for use in the matching of elevation maps. Using this feature, outdoor localization can be performed with an elevation map without external positioning systems. In this research, the Monte Carlo localization (MCL) method is used for outdoor localization, and the conventional method is based on range matching, which compares range sensor data with the range data predicted from an elevation map. Our proposed method is based on EMOI matching. The EMOI around a robot is compared with the EMOIs for all cells of the pregiven reference elevation map to find a robot pose with respect to the reference map. MCL based on EMOI matching is very fast, although its accuracy is slightly lower than that of conventional range matching. To deal with the disadvantage of EMOI matching, an adaptive switching scheme between EMOI matching and range matching was also proposed. Various outdoor experiments indicated that the proposed EMOI significantly reduced the convergence time of MCL. Therefore, the proposed feature is considered to be useful when an elevation map is used for outdoor localization. © 2010 Wiley Periodicals, Inc.