Lampposts as Landmarks for Simultaneous Localization and Mapping

2011 ◽  
Vol 403-408 ◽  
pp. 823-829
Author(s):  
Musfira Jilani ◽  
Padraig Corcoran ◽  
Peter Mooney
Keyword(s):  
High Frequency ◽  
Simultaneous Localization And Mapping ◽  
Ground Truth ◽  
University Campus ◽  
Binary Classifier ◽  
Icp Algorithm ◽  
Localization And Mapping ◽  
Three Stages ◽  
Spatial Size ◽  
Slam Algorithm

This paper investigates the effectiveness of using lampposts, which are commonly found in University campus environments with high frequency, as landmarks in a 2D LIDAR based Simultaneous Localization and Mapping (SLAM) framework. Lampposts offer a number of benefits compared to other forms of landmarks. Their unique spatial signature makes it possible to design effective algorithms to extract them. They have a very small spatial size. Their use removes the challenge of determining a corresponding location between difference views. This represents a major challenge if larger objects are used as landmarks. The proposed SLAM algorithm contains three stages. Firstly LIDAR segmentation is performed. Next each object is input to a binary classifier which determines objects with a high probability of corresponding to lampposts. Finally these extracted lampposts are input to an Iterative Closest Point (ICP) based SLAM algorithm. The ICP algorithm used is an extension of the traditional ICP algorithm and filters associations due to noise. Results achieved by the proposed system were very positive. An accurate map of a university’s lampposts was created and localization, when compared to GPS ground-truth, was very accurate.

Comparison of different SLAM approaches for a driverless race car

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nick Le Large ◽  
Frank Bieder ◽  
Martin Lauer
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Ground Truth ◽  
Time Constraints ◽  
Differential Gps ◽  
Real World Data ◽  
Race Car ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Computational Resources

Abstract For the application of an automated, driverless race car, we aim to assure high map and localization quality for successful driving on previously unknown, narrow race tracks. To achieve this goal, it is essential to choose an algorithm that fulfills the requirements in terms of accuracy, computational resources and run time. We propose both a filter-based and a smoothing-based Simultaneous Localization and Mapping (SLAM) algorithm and evaluate them using real-world data collected by a Formula Student Driverless race car. The accuracy is measured by comparing the SLAM-generated map to a ground truth map which was acquired using high-precision Differential GPS (DGPS) measurements. The results of the evaluation show that both algorithms meet required time constraints thanks to a parallelized architecture, with GraphSLAM draining the computational resources much faster than Extended Kalman Filter (EKF) SLAM. However, the analysis of the maps generated by the algorithms shows that GraphSLAM outperforms EKF SLAM in terms of accuracy.

TSLAM: Tethered simultaneous localization and mapping for mobile robots

2017 ◽  
Vol 36 (12) ◽  
pp. 1363-1386 ◽  
Author(s):  
Patrick McGarey ◽  
Kirk MacTavish ◽  
François Pomerleau ◽  
Timothy D Barfoot
Keyword(s):  
Mobile Robots ◽  
Particle Filter ◽  
Low Cost ◽  
Simultaneous Localization And Mapping ◽  
Ground Truth ◽  
Anchor Point ◽  
Outlier Rejection ◽  
Cluttered Environments ◽  
Localization And Mapping ◽  
Anchor Points

Tethered mobile robots are useful for exploration in steep, rugged, and dangerous terrain. A tether can provide a robot with robust communications, power, and mechanical support, but also constrains motion. In cluttered environments, the tether will wrap around a number of intermediate ‘anchor points’, complicating navigation. We show that by measuring the length of tether deployed and the bearing to the most recent anchor point, we can formulate a tethered simultaneous localization and mapping (TSLAM) problem that allows us to estimate the pose of the robot and the positions of the anchor points, using only low-cost, nonvisual sensors. This information is used by the robot to safely return along an outgoing trajectory while avoiding tether entanglement. We are motivated by TSLAM as a building block to aid conventional, camera, and laser-based approaches to simultaneous localization and mapping (SLAM), which tend to fail in dark and or dusty environments. Unlike conventional range-bearing SLAM, the TSLAM problem must account for the fact that the tether-length measurements are a function of the robot’s pose and all the intermediate anchor-point positions. While this fact has implications on the sparsity that can be exploited in our method, we show that a solution to the TSLAM problem can still be found and formulate two approaches: (i) an online particle filter based on FastSLAM and (ii) an efficient, offline batch solution. We demonstrate that either method outperforms odometry alone, both in simulation and in experiments using our TReX (Tethered Robotic eXplorer) mobile robot operating in flat-indoor and steep-outdoor environments. For the indoor experiment, we compare each method using the same dataset with ground truth, showing that batch TSLAM outperforms particle-filter TSLAM in localization and mapping accuracy, owing to superior anchor-point detection, data association, and outlier rejection.

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.

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 Multi-Objective Optimization of Loop Closure Detection Parameters for Indoor 2D Simultaneous Localization and Mapping

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1906
Author(s):  
Dongxiao Han ◽  
Yuwen Li ◽  
Tao Song ◽  
Zhenyang Liu
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Ground Truth ◽  
Optimization Method ◽  
Evaluation Metrics ◽  
Multi Objective Optimization ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Multi Objective ◽  
Evaluation Approach ◽  
Localization And Mapping

Aiming at addressing the issues related to the tuning of loop closure detection parameters for indoor 2D graph-based simultaneous localization and mapping (SLAM), this article proposes a multi-objective optimization method for these parameters. The proposed method unifies the Karto SLAM algorithm, an efficient evaluation approach for map quality with three quantitative metrics, and a multi-objective optimization algorithm. More particularly, the evaluation metrics, i.e., the proportion of occupied grids, the number of corners and the amount of enclosed areas, can reflect the errors such as overlaps, blurring and misalignment when mapping nested loops, even in the absence of ground truth. The proposed method has been implemented and validated by testing on four datasets and two real-world environments. For all these tests, the map quality can be improved using the proposed method. Only loop closure detection parameters have been considered in this article, but the proposed evaluation metrics and optimization method have potential applications in the automatic tuning of other SLAM parameters to improve the map quality.

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.

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 A Novel RGB-D SLAM Algorithm Based on Cloud Robotics

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5288 ◽  
Author(s):  
Yanli Liu ◽  
Heng Zhang ◽  
Chao Huang
Keyword(s):  
Feature Extraction ◽  
Point Cloud ◽  
Experimental Validation ◽  
Simultaneous Localization And Mapping ◽  
Point Cloud Registration ◽  
Cloud Robotics ◽  
Localization And Mapping ◽  
Computationally Intensive ◽  
Pose Optimization ◽  
Slam Algorithm

In this paper, we present a novel red-green-blue-depth simultaneous localization and mapping (RGB-D SLAM) algorithm based on cloud robotics, which combines RGB-D SLAM with the cloud robot and offloads the back-end process of the RGB-D SLAM algorithm to the cloud. This paper analyzes the front and back parts of the original RGB-D SLAM algorithm and improves the algorithm from three aspects: feature extraction, point cloud registration, and pose optimization. Experiments show the superiority of the improved algorithm. In addition, taking advantage of the cloud robotics, the RGB-D SLAM algorithm is combined with the cloud robot and the back-end part of the computationally intensive algorithm is offloaded to the cloud. Experimental validation is provided, which compares the cloud robotic-based RGB-D SLAM algorithm with the local RGB-D SLAM algorithm. The results of the experiments demonstrate the superiority of our framework. The combination of cloud robotics and RGB-D SLAM can not only improve the efficiency of SLAM but also reduce the robot’s price and size.

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