scholarly journals An Ant–Based Filtering Random–Finite–Set Approach to Simultaneous Localization and Mapping

2018 ◽  
Vol 28 (3) ◽  
pp. 505-519
Author(s):  
Demeng Li ◽  
Jihong Zhua ◽  
Benlian Xu ◽  
Mingli Lu ◽  
Mingyue Li
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Posterior Density ◽  
Operation System ◽  
Feature Map ◽  
Localization And Mapping ◽  
Finite Set ◽  
Feature Based ◽  
Vehicle Trajectory ◽  
Slam Algorithm ◽  
Random Finite Sets

Abstract Inspired by ant foraging, as well as modeling of the feature map and measurements as random finite sets, a novel formulation in an ant colony framework is proposed to jointly estimate the map and the vehicle trajectory so as to solve a feature-based simultaneous localization and mapping (SLAM) problem. This so-called ant-PHD-SLAM algorithm allows decomposing the recursion for the joint map-trajectory posterior density into a jointly propagated posterior density of the vehicle trajectory and the posterior density of the feature map conditioned on the vehicle trajectory. More specifically, an ant-PHD filter is proposed to jointly estimate the number of map features and their locations, namely, using the powerful search ability and collective cooperation of ants to complete the PHD-SLAM filter time prediction and data update process. Meanwhile, a novel fast moving ant estimator (F-MAE) is utilized to estimate the maneuvering vehicle trajectory. Evaluation and comparison using several numerical examples show a performance improvement over recently reported approaches. Moreover, the experimental results based on the robot operation system (ROS) platform validate the consistency with the results obtained from numerical simulations.

Efficient Simultaneous Localization and Mapping Based on Ceiling-View: Ceiling Boundary Feature Map Approach

2012 ◽  
Vol 26 (5-6) ◽  
pp. 653-671 ◽  
Author(s):  
Hyukdoo Choi ◽  
Dong Yeop Kim ◽  
Jae Pil Hwang ◽  
Chang-Woo Park ◽  
Euntai Kim
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Feature Map ◽  
Localization And Mapping ◽  
Map Approach

scholarly journals REINFORCEMENT LEARNING HELPS SLAM: LEARNING TO BUILD MAPS

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 329-335
Author(s):  
N. Botteghi ◽  
B. Sirmacek ◽  
R. Schulte ◽  
M. Poel ◽  
C. Brune
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
A Priori ◽  
Simultaneous Localization And Mapping ◽  
Indoor Environments ◽  
Robot Localization ◽  
Robust Solution ◽  
Localization And Mapping ◽  
Reward Functions ◽  
Slam Algorithm

Abstract. In this research, we investigate the use of Reinforcement Learning (RL) for an effective and robust solution for exploring unknown and indoor environments and reconstructing their maps. We benefit from a Simultaneous Localization and Mapping (SLAM) algorithm for real-time robot localization and mapping. Three different reward functions are compared and tested in different environments with growing complexity. The performances of the three different RL-based path planners are assessed not only on the training environments, but also on an a priori unseen environment to test the generalization properties of the policies. The results indicate that RL-based planners trained to maximize the coverage of the map are able to consistently explore and construct the maps of different indoor environments.

scholarly journals Visual Simultaneous Localization and Mapping (vSLAM) of Driverless Car in GPS-Denied Areas

2021 ◽  
Vol 12 (1) ◽  
pp. 49
Author(s):  
Abira Kanwal ◽  
Zunaira Anjum ◽  
Wasif Muhammad
Keyword(s):  
Science Fiction ◽  
Urban Areas ◽  
Visual Information ◽  
Dynamic Environment ◽  
Simultaneous Localization And Mapping ◽  
Video Camera ◽  
Input Image ◽  
Localization And Mapping ◽  
Health Related ◽  
Slam Algorithm

A simultaneous localization and mapping (SLAM) algorithm allows a mobile robot or a driverless car to determine its location in an unknown and dynamic environment where it is placed, and simultaneously allows it to build a consistent map of that environment. Driverless cars are becoming an emerging reality from science fiction, but there is still too much required for the development of technological breakthroughs for their control, guidance, safety, and health related issues. One existing problem which is required to be addressed is SLAM of driverless car in GPS denied-areas, i.e., congested urban areas with large buildings where GPS signals are weak as a result of congested infrastructure. Due to poor reception of GPS signals in these areas, there is an immense need to localize and route driverless car using onboard sensory modalities, e.g., LIDAR, RADAR, etc., without being dependent on GPS information for its navigation and control. The driverless car SLAM using LIDAR and RADAR involves costly sensors, which appears to be a limitation of this approach. To overcome these limitations, in this article we propose a visual information-based SLAM (vSLAM) algorithm for GPS-denied areas using a cheap video camera. As a front-end process, features-based monocular visual odometry (VO) on grayscale input image frames is performed. Random Sample Consensus (RANSAC) refinement and global pose estimation is performed as a back-end process. The results obtained from the proposed approach demonstrate 95% accuracy with a maximum mean error of 4.98.

UAV Active SLAM Trajectory Programming Based on Optimal Control

2013 ◽  
Vol 765-767 ◽  
pp. 1932-1935
Author(s):  
Zeng Xiang Yang ◽  
Sai Jin
Keyword(s):  
Optimal Control ◽  
Optimization Problem ◽  
Simultaneous Localization And Mapping ◽  
Plane Motion ◽  
Programming Algorithm ◽  
Motion Model ◽  
Unknown Environment ◽  
Localization And Mapping ◽  
Simulation Results ◽  
Slam Algorithm

To decrease the uncertainty of simultaneous localization and mapping of UAV, and at the same time, to increase the speed of searching the unknown environment at which UAV locates, an active SLAM trajectory programming algorithm is proposed based on optimal control. Therefore, UAV SLAM is tackled as a combined optimization problem, considering the precision of UAV location and mapping integrity. Based on the simplified UAV plane motion model, this algorithm is simulated and tested by comparing with the random SLAM algorithm. Simulation results show that this algorithm is effective.

scholarly journals Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Inam Ullah ◽  
Xin Su ◽  
Xuewu Zhang ◽  
Dongmin Choi
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Extended Kalman Filter ◽  
Simultaneous Localization And Mapping ◽  
Absolute Measurement ◽  
Relative Measurement ◽  
Localization Algorithm ◽  
Moving Vehicle ◽  
Localization And Mapping ◽  
Slam Algorithm

For more than two decades, the issue of simultaneous localization and mapping (SLAM) has gained more attention from researchers and remains an influential topic in robotics. Currently, various algorithms of the mobile robot SLAM have been investigated. However, the probability-based mobile robot SLAM algorithm is often used in the unknown environment. In this paper, the authors proposed two main algorithms of localization. First is the linear Kalman Filter (KF) SLAM, which consists of five phases, such as (a) motionless robot with absolute measurement, (b) moving vehicle with absolute measurement, (c) motionless robot with relative measurement, (d) moving vehicle with relative measurement, and (e) moving vehicle with relative measurement while the robot location is not detected. The second localization algorithm is the SLAM with the Extended Kalman Filter (EKF). Finally, the proposed SLAM algorithms are tested by simulations to be efficient and viable. The simulation results show that the presented SLAM approaches can accurately locate the landmark and mobile robot.

scholarly journals Implementation and Reconfiguration of Robot Operating System on Human Follower Transporter Robot

2015 ◽  
Vol 9 (2) ◽  
pp. 59
Author(s):  
Addythia Saphala ◽  
Prianggada Indra Tanaya
Keyword(s):  
Operating System ◽  
Mobile Robot ◽  
Navigation System ◽  
Robot Navigation ◽  
Simultaneous Localization And Mapping ◽  
Kinect Sensor ◽  
Operation System ◽  
Robot Operating System ◽  
Localization And Mapping

Robotic Operation System (ROS) is an im- portant platform to develop robot applications. One area of applications is for development of a Human Follower Transporter Robot (HFTR), which  can  be  considered  as a custom mobile robot utilizing differential driver steering method and equipped with Kinect sensor. This study discusses the development of the robot navigation system by implementing Simultaneous Localization and Mapping (SLAM).

Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation

2012 ◽  
Vol 28 (1) ◽  
pp. 35-57 ◽  
Author(s):  
Fernando A. Auat Cheein ◽  
Fernando M. Lobo Pereira ◽  
Fernando di Sciascio ◽  
Ricardo Carelli
Keyword(s):  
Monte Carlo ◽  
Mobile Robot ◽  
Simultaneous Localization And Mapping ◽  
Path Following ◽  
Monte Carlo Experiment ◽  
Localization And Mapping ◽  
Trajectory Following ◽  
Uncertainty Map ◽  
Slam Algorithm ◽  
Representative Points

AbstractThis paper addresses the problem of implementing a Simultaneous Localization and Mapping (SLAM) algorithm combined with a non-reactive controller (such as trajectory following or path following). A general study showing the advantages of using predictors to avoid mapping inconsistences in autonomous SLAM architectures is presented. In addition, this paper presents a priority-based uncertainty map construction method of the environment by a mobile robot when executing a SLAM algorithm. The SLAM algorithm is implemented with an extended Kalman filter (EKF) and extracts corners (convex and concave) and lines (associated with walls) from the surrounding environment. A navigation approach directs the robot motion to the regions of the environment with the higher uncertainty and the higher priority. The uncertainty of a region is specified by a probability characterization computed at the corresponding representative points. These points are obtained by a Monte Carlo experiment and their probability is estimated by the sum of Gaussians method, avoiding the time-consuming map-gridding procedure. The priority is determined by the frame in which the uncertainty region was detected (either local or global to the vehicle's pose). The mobile robot has a non-reactive trajectory following controller implemented on it to drive the vehicle to the uncertainty points. SLAM real-time experiments in real environment, navigation examples, uncertainty maps constructions along with algorithm strategies and architectures are also included in this work.

scholarly journals Simultaneous localization and mapping: A feature-based probabilistic approach

2009 ◽  
Vol 19 (4) ◽  
pp. 575-588 ◽  
Author(s):  
Piotr Skrzypczyński
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Data Association ◽  
Probabilistic Approach ◽  
Simultaneous Localization And Mapping ◽  
Probabilistic Formulation ◽  
Recent Advances ◽  
Localization And Mapping ◽  
Feature Based ◽  
Points Of View

Simultaneous localization and mapping: A feature-based probabilistic approachThis article provides an introduction to Simultaneous Localization And Mapping (SLAM), with the focus on probabilistic SLAM utilizing a feature-based description of the environment. A probabilistic formulation of the SLAM problem is introduced, and a solution based on the Extended Kalman Filter (EKF-SLAM) is shown. Important issues of convergence, consistency, observability, data association and scaling in EKF-SLAM are discussed from both theoretical and practical points of view. Major extensions to the basic EKF-SLAM method and some recent advances in SLAM are also presented.

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