Adaptive Monte Carlo Localization Research Articles

Enhanced Localization with Adaptive Normal Distribution Transform Monte Carlo Localization for Map Based Navigation Robot

Map-based navigation is the common navigation method used among the mobile robotic application. The localization plays an important role in the navigation where it estimates the robot position in an environment. Monte Carlo Localization (MCL) is found as the widely used estimation algorithm due to it non-linear characteristic. There are classifications of MCL such as Adaptive MCL (AMCL), Normal Distribution Transform MCL (NDT-MCL) which can perform better than the MCL. However, AMCL is adaptive to particles but the position estimation accuracy is not optimized. NDT-MCL has good position estimation but it requires higher number of particles which results in higher computational effort. The objective of the research is to design and develop a localization algorithm which can achieve better performance in term of position estimation and computational effort. The new MCL algorithm which is named as Adaptive Normal Distribution Transform Monte Carlo Localization (ANDT-MCL) is then designed and developed. It integrates Kullback–Leibler divergence, Normal Distribution Transform and Systematic Resampling into the algorithm. Three experiments are conducted to evaluate the performance of proposed ANDT-MCL in simulated environment. These experiments include evaluating the performance of ANDT-MCL with different path shape, distance and velocity. In the end of the research work, the proposed ANDT-MCL is successfully developed. It is adaptive to the number of particles used, higher position estimation and lower computational effort than existing algorithms. The algorithm can produce better position estimation with less computational effort in any kind paths and is consistent in long journey as well as can outperform in high speed navigation.

A Novel Global Localization Approach Based on Structural Unit Encoding and Multiple Hypothesis Tracking

In this paper, we present a novel 2-D laser-based global localization approach for mobile robots, which is composed of geometrical relationship construction, a new structural unit encoding scheme (SUES), and an extended multiple hypothesis tracking (MHT) algorithm. Different from existing methods, we construct a 3-D “directional endpoint” feature encapsulating both the endpoint and the direction of a line segment; on this basis, a novel and efficient online structural unit encoding scheme (SUES) is proposed to describe the geometric relationship between the two directional endpoint features with some robustness to dynamic disturbances. Note that SUES presented in this paper is different from the bag-of-words scheme in two aspects: 1) SUES quantizes the geometrical relationship without offline training for vocabulary and 2) SUES is independent of the quality of the vocabulary. By factoring the global localization problem into a discrete pose estimation problem, the MHT is extended on the basis of SUES and odometry information to gradually restore the global robot pose. Different from the classical MHT framework, the extended MHT takes the independent observation results as the a priori , while the likelihood term is composed of consecutive candidate poses and the odometry information. Evaluations are carried out by using both publicly available data sets and self-recorded data sets. Comparative experimental results with respect to the adaptive Monte Carlo localization are presented to show the superior performance of the proposed approach in terms of success ratio and efficiency.

SISTEM NAVIGASI MOBILE ROBOT DALAM RUANGAN BERBASIS AUTONOMOUS NAVIGATION

<p><em>Autonomous navigation is absolutely necessary in mobile-robotic, which consists of four main components, namely: perception, localization, path-planning, and motion-control. Mobile robots create maps of space so that they can carry out commands to move from one place to another using the autonomous-navigation method. Map making using the Simultaneous-Localization-and-Mapping (SLAM) algorithm that processes data from the RGB-D camera sensor and bumper converted to laser-scan and point-cloud is used to obtain perception. While the wheel-encoder and gyroscope are used to obtain odometry data which is used to construct travel maps with the SLAM algorithm, gmapping and performing autonomous navigation. The system consists of three sub-systems, namely: sensors as inputs, single-board computers for processes, and actuators as movers. Autonomous-navigation is regulated through the navigation-stack using the Adaptive-Monte-Carlo-Localization (AMCL) algorithm for localization and global-planning, while the Dynamic-Window-Approach (DWA) algorithm with Robot-Operating-System-(ROS) for local -planning. The results of the test show the system can provide depth-data that is converted to laser-scan, bumper data, and odometry data to single-board-computer-based ROS so that mobile-controlled teleoperating robots from workstations can build 2-dimensional grid maps with total accuracy error rate of 0.987%. By using maps, data from sensors, and odometry the mobile-robot can perform autonomous-navigation consistently and be able to do path-replanning, avoid static obstacles and continue to do localization to reach the destination point.</em></p>

Research on Improved Localization and Navigation Algorithm for Automatic Guided Vehicle

Aiming at the problem of inaccurate positioning caused by the wheel slippage or “kidnapping” movement of the robot in the positioning navigation, an improved autonomous positioning navigation strategy based on the robot operating system (ROS) is proposed. Firstly, combined with adaptive Monte Carlo localization (AMCL) algorithm and laser-based point-and-line iterative closest/corresponding point (PLICP) pose estimation algorithm, the accuracy and robustness of positioning are effectively improved. Then, based on the path planning strategy combining A* algorithm and dynamic window algorithm (DWA), an improved navigation failure recovery method is proposed and integrated into the ROS navigation framework, which can effectively improve the efficiency of robot positioning navigation and task execution. Finally, the mobile robot model TurtleBot is used as the experimental platform. The simulation experiment and field test demonstrate that the improved algorithm is superior to the original algorithm. The improved algorithm can adapt to the inaccuracy of the odometer and can achieve accurate localization and navigation in long-distance environment.

Self-adaptive Monte Carlo method for indoor localization of smart AGVs using LIDAR data

The vehicles used for transportation and logistics in the factories usually perceive their surroundings with range sensors. Today, 2D LIDARs are used as range sensors, and 3D LIDARs are becoming widespread with the developments of autonomous vehicle technology. Therefore, Self Adaptive Monte Carlo Localization, abbreviated as SA-MCL, is improved in this study to make the algorithm suitable for autonomous guided vehicles (AGVs) equipped with 2D or 3D LIDARs. Moreover, the traditional SA-MCL algorithm has a constraint that the range sensors on the robot are uniformly placed, and ellipse based energy model is proposed in this study to remove the constraint. This model can compute the energy value regardless of the robot orientation since it considers offsets due to the asymmetric placement of range sensors on the robot. The importance of localization increases since it is aimed that AGVs to be used in smart factories are able to use entire free space on the map in order to provide energy efficiency and time saving, and perform tasks that can vary at anytime instead of routine. SA-MCL algorithm is preferred in this study since traditional SA-MCL can overcome global localization, position tracking and kidnapping sub-problems of localization. The algorithm proposed in this study is verified to demonstrate its performance and effectiveness both in simulation and experimental studies using MATLAB and robot operating system (ROS).

Reliable and Fast Localization in Ambiguous Environments Using Ambiguity Grid Map

In real-world robotic navigation, some ambiguous environments contain symmetrical or featureless areas that may cause the perceptual aliasing of external sensors. As a result of that, the uncorrected localization errors will accumulate during the localization process, which imposes difficulties to locate a robot in such a situation. Using the ambiguity grid map (AGM), we address this problem by proposing a novel probabilistic localization method, referred to as AGM-based adaptive Monte Carlo localization. AGM has the capacity of evaluating the environmental ambiguity with average ambiguity error and estimating the possible localization error at a given pose. Benefiting from the constructed AGM, our localization method is derived from an improved Dynamic Bayes network to reason about the robot’s pose as well as the accumulated localization error. Moreover, a portal motion model is presented to achieve more reliable pose prediction without time-consuming implementation, and thus the accumulated localization error can be corrected immediately when the robot moving through an ambiguous area. Simulation and real-world experiments demonstrate that the proposed method improves localization reliability while maintains efficiency in ambiguous environments.

Mobile Robot Localization Based on the Novel Leader-Based Bat Algorithm

This work proposes a new approach to the well-known method bat algorithm for solving the mobile robots global localization problem. The proposed method is leader-based bat algorithm (LBBA). The LBBA uses a small number of better micro-bats as leaders to influence the colony in the search for the best position, dealing satisfactorily with ambiguities during the localization process. The tests covered different scenarios aiming at comparing the proposed algorithm with other methods, such as the standard BA, the particle swarm optimization and particle filter. The results outperformed the compared methods, presenting a fast response and errors below the intended tolerance. The algorithm was tested in the robot kidnapping scenario and shows fast recovery in both simulation and in a real environment. In addition, the proposed technique showed 21% lower average error when compared with an algorithm that presents a variable quantity of particles, i.e. the adaptive Monte Carlo localization algorithm.

A Robust Indoor Localization System Integrating Visual Localization Aided by CNN-Based Image Retrieval with Monte Carlo Localization.

This paper proposes a novel multi-sensor-based indoor global localization system integrating visual localization aided by CNN-based image retrieval with a probabilistic localization approach. The global localization system consists of three parts: coarse place recognition, fine localization and re-localization from kidnapping. Coarse place recognition exploits a monocular camera to realize the initial localization based on image retrieval, in which off-the-shelf features extracted from a pre-trained Convolutional Neural Network (CNN) are adopted to determine the candidate locations of the robot. In the fine localization, a laser range finder is equipped to estimate the accurate pose of a mobile robot by means of an adaptive Monte Carlo localization, in which the candidate locations obtained by image retrieval are considered as seeds for initial random sampling. Additionally, to address the problem of robot kidnapping, we present a closed-loop localization mechanism to monitor the state of the robot in real time and make adaptive adjustments when the robot is kidnapped. The closed-loop mechanism effectively exploits the correlation of image sequences to realize the re-localization based on Long-Short Term Memory (LSTM) network. Extensive experiments were conducted and the results indicate that the proposed method not only exhibits great improvement on accuracy and speed, but also can recover from localization failures compared to two conventional localization methods.

ROS-based localization of a race vehicle at high-speed using LIDAR

An approach for LIDAR-based localization at high speeds is presented. In the proposed framework, the laser pose estimation is treated as a parallel redundant information, which is fused in an adjacent Kalman filter. The measurement and motion update step of the ROS-based adaptive Monte Carlo localization package is modified, in order to meet the requirements of a high-speed race scenario. Thereby, the key focus is on computational efficiency and the adaptation to characteristics arising at high speeds and at the limits of handling. An introspective performance evaluation monitors the position estimation process and labels generated outputs for adjacent components accordingly. The effectiveness of the proposed algorithm is illustrated in a real world high-speed experiment, autonomously driving a race vehicle – the DevBot – in a typical race environment.

An Improved AMCL Algorithm Based on Laser Scanning Match in a Complex and Unstructured Environment

Adaptive Monte Carlo localization (AMCL) algorithm has a limited pose accuracy because of the nonconvexity of the laser sensor model, the complex and unstructured features of the working environment, the randomness of particle sampling, and the final pose selection problem. In this paper, an improved AMCL algorithm is proposed, aiming to build a laser radar-based robot localization system in a complex and unstructured environment, with a LIDAR point cloud scan-matching process after the particle score calculating process. The weighted mean pose of AMCL particle swarm is used as the initial pose of the scan matching process. The LIDAR point cloud is matched with the probability grid map from coarse to fine using the Gaussian-Newton method, which results in more accurate poses. Moreover, the scan-matching pose is added into the particle swarm as a high-weight particle. So the particle swarm after resampling will be more concentrated in the correct position. The particle filter and the scan-matching process form a closed loop, thus enhancing the localization accuracy of mobile robots. The experiment results demonstrate that the proposed improved AMCL algorithm is superior to the traditional AMCL algorithm in the complex and unstructured environment, by exploiting the high-accuracy characteristic of scan matching while inheriting the stability of AMCL.

Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation.

In the case of a single scene feature, the positioning of an indoor service robot takes a long time, and localization errors are likely to occur. A new method for a hybrid indoor localization system according to multi-sensor fusion is proposed to solve these problems. The localization process is divided in two stages: rough positioning and precise positioning. By virtue of the K nearest neighbors based on possibility (KNNBP) algorithm first created in the present study, the rough position of a robot is determined according to the received signal strength indicator (RSSI) of Wi-Fi. Then, the hybrid particle filter localization (HPFL) algorithm improved on the basis of adaptive Monte Carlo localization (AMCL) is adopted to get the precise localization, which integrates various information, including the rough position and information from Lidar, a compass, an occupancy grid map, and encoders. The experiments indicated that the positioning error was 0.05 m; the success rate of localization was 96% with even 3000 particles, and the global positioning time was 1.9 s. However, under the same conditions, the success rate of AMCL was approximately 40%, the required time was approximately 25.6 s, and the positioning accuracy was the same. This indicates that the hybrid indoor location system is efficient and accurate.

Vineyard Autonomous Navigation in the Echord++ GRAPE Experiment

Field robotics is a fast developing research field, in particular precision agriculture is gaining in popularity due to the high return in productivity and the reduced pollution impact on the environment. The GRAPE project is an ECHORD++ robotic experiment aimed at the use of a mobile robot for automatic pheromone dispenser distribution in vineyards, to reduce pesticide use thanks to pheromone mate disruption. This work describes the autonomous navigation system of such robot. For the specific scenario a real state of the art does not exists, so we adapted techniques that have been designed for different problems, in particular classical methods for navigation and mapping in indoor environments. The vineyard environment is challenging because of many variability factors such as weather, soil and vegetation. These factors hinder the indoor methods introducing noise in the robot perceptions. To solve this problem, we propose a specific navigation system that takes advantage of multiple sensors: wheel encoders, inertial measurement unit (IMU), and GPS to filter the environment noise and accurately estimate the robot odometry. In addition, the system exploits a LIDAR sensor to localize the robot, through the Adaptive Monte Carlo Localization (AMCL) algorithm, and to map the vineyard. We tested the system in simulation with very good results which have been confirmed during field tests in a real vineyard.

DESIGN AND DEVELOPMENT OF AN AUTONOMOUS OMNIDIRECTIONAL HAZARDOUS MATERIALS HANDLING ROBOT

This paper describes the design and testing of an autonomous omnidirectional robot to be used for moving radioactive materials while minimizing human exposure. The robot, called the OmniMaxbot, uses the Robot Operating System (ROS) to allow the individual components to communicate as well as to control the movement. Details about the hardware and software used in the OmniMaxbot are explained. Test results are presented for the navigation system based on the ROS packages: Global Planner, Base Local Planner, and Adaptive Monte Carlo Localization (AMCL). The test results confirm that the OmniMaxbot is capable of autonomously navigating to a mock ash can, lifting it, navigating to a drop-off location, putting the mock ash can down, backing away until the forks are clear of the mock ash can, and navigating to a standby location. These actions can be performed in areas with both static and dynamic obstacles.

A Method for Active Global Localization in Multi-robot System

In multi-robot system the ability to exchange information can reduce the uncertainty in the estimated location when robots can see each other. In this paper, a kind of dynamically evolving coordination architecture is proposed for cooperative localization according to the relative positions between robots. And to further improve the efficiency of cooperative localization, a decision theory based mechanism is proposed to make the robots cooperate actively during the localization process. Since stably tracking the multi-hypothesis of the robots' own position and their partners' position is of great importance for making a good decision of where to go in active localization, the co-evolution based adaptive Monte Carlo localization method in which samples are clustered into species to represents a hypothesis of robot's pose in a higher level than a single sample is adopted. Experiments are designed and carried out to prove the efficiency and stability of the proposed method.

Coevolution Based Adaptive Monte Carlo Localization (CEAMCL)

An adaptive Monte Carlo localization algorithm based on coevolution mechanism of ecological species is proposed. Samples are clustered into species, each of which represents a hypothesis of the robot's pose. Since the coevolution between the species ensures that the multiple distinct hypotheses can be tracked stably, the problem of premature convergence when using MCL in highly symmetric environments can be solved. And the sample size can be adjusted adaptively over time according to the uncertainty of the robot's pose by using the population growth model. In addition, by using the crossover and mutation operators in evolutionary computation, intra-species evolution can drive the samples move towards the regions where the desired posterior density is large. So a small size of samples can represent the desired density well enough to make precise localization. The new algorithm is termed coevolution based adaptive Monte Carlo localization (CEAMCL). Experiments have been carried out to prove the efficiency of the new localization algorithm.