scholarly journals Navigation Strategies for Exploring Indoor Environments

2002 ◽  
Vol 21 (10-11) ◽  
pp. 829-848 ◽  
Author(s):  
Héctor H. González-Baños ◽  
Jean-Claude Latombe
Keyword(s):  
Mobile Robotics ◽  
Local Model ◽  
Indoor Environments ◽  
Main Question ◽  
Map Building ◽  
Sensory Data ◽  
Localization And Mapping ◽  
Expected Gain ◽  
Safe Region ◽  
Slam Algorithm

In this paper, we investigate safe and efficient map-building strategies for a mobile robot with imperfect control and sensing. In the implementation, a robot equipped with a range sensor builds apolygonal map (layout) of a previously unknown indoor environment. The robot explores the environment and builds the map concurrently by patching together the local models acquired by the sensor into a global map. A well-studied and related problem is the simultaneous localization and mapping (SLAM) problem, where the goal is to integrate the information collected during navigation into the most accurate map possible. However, SLAM does not address the sensor-placement portion of the map-building task. That is, given the map built so far, where should the robot go next? This is the main question addressed in this paper. Concretely, an algorithm is proposed to guide the robot through a series of “good” positions, where “good” refers to the expected amount and quality of the information that will be revealed at each new location. This is similar to the next-best-view (NBV) problem studied in computer vision and graphics. However, in mobile robotics the problem is complicated by several issues, two of which are particularly crucial. One is to achieve safe navigation despite an incomplete knowledge of the environment and sensor limitations (e.g., in range and incidence). The other issue is the need to ensure sufficient overlap between each new local model and the current map, in order to allow registration of successive views under positioning uncertainties inherent to mobile robots. To address both issues in a coherent framework, in this paper we introduce the concept of a safe region, defined as the largest region that is guaranteed to be free of obstacles given the sensor readings made so far. The construction of a safe region takes sensor limitations into account. In this paper we also describe an NBV algorithm that uses the safe-region concept to select the next robot position at each step. The new position is chosen within the safe region in order to maximize the expected gain of information under the constraint that the local model at this new position must have a minimal overlap with the current global map. In the future, NBV and SLAM algorithms should reinforce each other. While a SLAM algorithm builds a map by making the best use of the available sensory data, an NBV algorithm, such as that proposed here, guides the navigation of the robot through positions selected to provide the best sensory inputs.

scholarly journals VECTOR MAPS IN MOBILE ROBOTICS

2015 ◽  
Vol 2 (2) ◽  
pp. 22-28 ◽  
Author(s):  
Ales Jelinek
Keyword(s):  
Data Acquisition ◽  
Navigation System ◽  
Mobile Robotics ◽  
Map Building ◽  
Scan Matching ◽  
Line Extraction ◽  
Current State ◽  
Vector Maps ◽  
Localization And Mapping ◽  
University Of Technology

The aim of this paper is to provide a brief overview of vector map techniques used in mobile robotics and to present current state of the research in this field at the Brno University of Technology. Vector maps are described as a part of the simultaneous localization and mapping (SLAM) problem in the environment without artificial landmarks or global navigation system. The paper describes algorithms from data acquisition to map building but particular emphasis is put on segmentation, line extraction and scan matching algorithms. All significant algorithms are illustrated with experimental results.

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 Successive Collaborative SLAM: Towards Reliable Inertial Pedestrian Navigation

Information ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 464
Author(s):  
Susanna Kaiser
Keyword(s):  
Terrorist Attack ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Multiple Sensors ◽  
Localization And Mapping ◽  
Window Technique ◽  
Measurement Units ◽  
The Mean ◽  
Slam Algorithm ◽  
Emergency Scenarios

In emergency scenarios, such as a terrorist attack or a building on fire, it is desirable to track first responders in order to coordinate the operation. Pedestrian tracking methods solely based on inertial measurement units in indoor environments are candidates for such operations since they do not depend on pre-installed infrastructure. A very powerful indoor navigation method represents collaborative simultaneous localization and mapping (collaborative SLAM), where the learned maps of several users can be combined in order to help indoor positioning. In this paper, maps are estimated from several similar trajectories (multiple users) or one user wearing multiple sensors. They are combined successively in order to obtain a precise map and positioning. For reducing complexity, the trajectories are divided into small portions (sliding window technique) and are partly successively applied to the collaborative SLAM algorithm. We investigate successive combinations of the map portions of several pedestrians and analyze the resulting position accuracy. The results depend on several parameters, e.g., the number of users or sensors, the sensor drifts, the amount of revisited area, the number of iterations, and the windows size. We provide a discussion about the choice of the parameters. The results show that the mean position error can be reduced to ≈0.5 m when applying partly successive collaborative SLAM.

Vision and laser fused SLAM in indoor environments with multi-robot system

2019 ◽  
Vol 39 (2) ◽  
pp. 297-307 ◽  
Author(s):  
Haoyao Chen ◽  
Hailin Huang ◽  
Ye Qin ◽  
Yanjie Li ◽  
Yunhui Liu
Keyword(s):  
Large Scale ◽  
Feature Detection ◽  
Mobile Robotics ◽  
Least Square ◽  
Indoor Environments ◽  
Fusion Algorithm ◽  
Content Type ◽  
Localization And Mapping ◽  
Loop Detection ◽  
Multi Robot

Purpose Multi-robot laser-based simultaneous localization and mapping (SLAM) in large-scale environments is an essential but challenging issue in mobile robotics, especially in situations wherein no prior knowledge is available between robots. Moreover, the cumulative errors of every individual robot exert a serious negative effect on loop detection and map fusion. To address these problems, this paper aims to propose an efficient approach that combines laser and vision measurements. Design/methodology/approach A multi-robot visual laser-SLAM is developed to realize robust and efficient SLAM in large-scale environments; both vision and laser loop detections are integrated to detect robust loops. A method based on oriented brief (ORB) feature detection and bag of words (BoW) is developed, to ensure the robustness and computational effectiveness of the multi-robot SLAM system. A robust and efficient graph fusion algorithm is proposed to merge pose graphs from different robots. Findings The proposed method can detect loops more quickly and accurately than the laser-only SLAM, and it can fuse the submaps of each single robot to promote the efficiency, accuracy and robustness of the system. Originality/value Compared with the state of art of multi-robot SLAM approaches, the paper proposed a novel and more sophisticated approach. The vision-based and laser-based loops are integrated to realize a robust loop detection. The ORB features and BoW technologies are further utilized to gain real-time performance. Finally, random sample consensus and least-square methodologies are used to remove the outlier loops among robots.

A two-level optimized graph-based simultaneous localization and mapping algorithm

2018 ◽  
Vol 45 (6) ◽  
pp. 758-765
Author(s):  
Hui Xiong ◽  
Youping Chen ◽  
Xiaoping Li ◽  
Bing Chen
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Environmental Information ◽  
Indoor Environments ◽  
Construction Process ◽  
Loop Closure ◽  
Content Type ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Estimated Error

PurposeBecause submaps including a subset of the global map contain more environmental information, submap-based graph simultaneous localization and mapping (SLAM) has been studied by many researchers. In most of those studies, helpful environmental information was not taken into consideration when designed the termination criterion of the submap construction process. After optimizing the graph, cumulative error within the submaps was also ignored. To address those problems, this paper aims to propose a two-level optimized graph-based SLAM algorithm.Design/methodology/approachSubmaps are updated by extended Kalman filter SLAM while no geometric-shaped landmark models are needed; raw laser scans are treated as landmarks. A more reasonable criterion called the uncertainty index is proposed to combine with the size of the submap to terminate the submap construction process. After a submap is completed and a loop closure is found, a two-level optimization process is performed to minimize the loop closure error and the accumulated error within the submaps.FindingsSimulation and experimental results indicate that the estimated error of the proposed algorithm is small, and the maps generated are consistent whether in global or local.Practical implicationsThe proposed method is robust to sparse pedestrians and can be adapted to most indoor environments.Originality/valueIn this paper, a two-level optimized graph-based SLAM algorithm is proposed.

scholarly journals Evaluation of Bio-inspired SLAM algorithm based on a Heterogeneous System CPU-GPU

2021 ◽  
Vol 229 ◽  
pp. 01023
Author(s):  
Rachid Latif ◽  
Kaoutar Dahmane ◽  
Monir Amraoui ◽  
Amine Saddik ◽  
Abdelouahed Elouardi
Keyword(s):  
Execution Time ◽  
Autonomous Navigation ◽  
Heterogeneous System ◽  
Mobile Robotics ◽  
Simultaneous Localization And Mapping ◽  
Real Problem ◽  
System Type ◽  
Localization And Mapping ◽  
Slam Algorithm

Localization and mapping are a real problem in robotics which has led the robotics community to propose solutions for this problem... Among the competitive axes of mobile robotics there is the autonomous navigation based on simultaneous localization and mapping (SLAM) algorithms: in order to have the capacity to track the localization and the cartography of robots, that give the machines the power to move in an autonomous environment. In this work we propose an implementation of the bio-inspired SLAM algorithm RatSLAM based on a heterogeneous system type CPU-GPU. The evaluation of the algorithm showed that with C/C++ we have an executing time of 170.611 ms with a processing of 5 frames/s and for the implementation on a heterogeneous system we used CUDA as language with an execution time of 160.43 ms.

scholarly journals TERRESTRIAL MOBILE MAPPING BASED ON A MICROWAVE RADAR SENSOR. APPLICATION TO THE LOCALIZATION OF MOBILE ROBOTS

2021 ◽  
Vol V-1-2021 ◽  
pp. 73-80
Author(s):  
R. Rouveure ◽  
P. Faure ◽  
M.-O. Monod
Keyword(s):  
Global Positioning System ◽  
Mobile Robotics ◽  
Ease Of Use ◽  
Mobile Mapping ◽  
Microwave Radar ◽  
Localization And Mapping ◽  
Global Positioning ◽  
Outdoor Environments ◽  
Slam Algorithm ◽  
Radar Map

Abstract. Mobile robotics applications in outdoor environments now use intensively Global Positioning System (GPS). For localization or navigation operations, GPS has become an essential tool due to its ease of use, its precision, and its worldwide accessibility. The increase of autonomy level in mobile robotics implies a robust centimeter-level positioning, but the presence of natural (trees, mountains) or man-made obstacles (buildings) can degrade or prevent GPS signals reception. We present in this paper a solution for robots localization based on PELICAN microwave radar. PELICAN radar provides each second a panoramic image of the surrounding environment. These images are concatenated through a Simultaneous Localization And Mapping (SLAM) algorithm in order to build global maps of the traveled environments. The proposed solution computes the position and orientation of the robot through a real-time 3D matching between the current radar image and a pre-existing radar map constructed during an exploratory phase.

scholarly journals An Adaptive Augmented Vision-Based Ellipsoidal SLAM for Indoor Environments

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2795
Author(s):  
Lahemer ◽  
Rad
Keyword(s):  
Real Time ◽  
Indoor Environment ◽  
Vision System ◽  
Computational Effort ◽  
Monocular Vision ◽  
Indoor Environments ◽  
Simulation Studies ◽  
Localization And Mapping ◽  
Landmark Identification ◽  
Slam Algorithm

In this paper, the problem of Simultaneous Localization And Mapping (SLAM) is addressed via a novel augmented landmark vision-based ellipsoidal SLAM. The algorithm is implemented on a NAO humanoid robot and is tested in an indoor environment. The main feature of the system is the implementation of SLAM with a monocular vision system. Distinguished landmarks referred to as NAOmarks are employed to localize the robot via its monocular vision system. We henceforth introduce the notion of robotic augmented reality (RAR) and present a monocular Extended Kalman Filter (EKF)/ellipsoidal SLAM in order to improve the performance and alleviate the computational effort, to provide landmark identification, and to simplify the data association problem. The proposed SLAM algorithm is implemented in real-time to further calibrate the ellipsoidal SLAM parameters, noise bounding, and to improve its overall accuracy. The augmented EKF/ellipsoidal SLAM algorithms are compared with the regular EKF/ellipsoidal SLAM methods and the merits of each algorithm is also discussed in the paper. The real-time experimental and simulation studies suggest that the adaptive augmented ellipsoidal SLAM is more accurate than the conventional EKF/ellipsoidal SLAMs.

scholarly journals RGB-D SLAM with Manhattan Frame Estimation Using Orientation Relevance

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1050 ◽  
Author(s):  
Liang Wang ◽  
Zhiqiu Wu
Keyword(s):  
Color Image ◽  
Image Noise ◽  
Indoor Environments ◽  
Spatial Transformation ◽  
Depth Data ◽  
Localization And Mapping ◽  
Key Points ◽  
Noise Image ◽  
Image Blur ◽  
Slam Algorithm

Due to image noise, image blur, and inconsistency between depth data and color image, the accuracy and robustness of the pairwise spatial transformation computed by matching extracted features of detected key points in existing sparse Red Green Blue-Depth (RGB-D) Simultaneously Localization And Mapping (SLAM) algorithms are poor. Considering that most indoor environments follow the Manhattan World assumption and the Manhattan Frame can be used as a reference to compute the pairwise spatial transformation, a new RGB-D SLAM algorithm is proposed. It first performs the Manhattan Frame Estimation using the introduced concept of orientation relevance. Then the pairwise spatial transformation between two RGB-D frames is computed with the Manhattan Frame Estimation. Finally, the Manhattan Frame Estimation using orientation relevance is incorporated into the RGB-D SLAM to improve its performance. Experimental results show that the proposed RGB-D SLAM algorithm has definite improvements in accuracy, robustness, and runtime.

scholarly journals A Light-and-Fast SLAM Algorithm for Robots in Indoor Environments Using Line Segment Map

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Bor-Woei Kuo ◽  
Hsun-Hao Chang ◽  
Yung-Chang Chen ◽  
Shi-Yu Huang
Keyword(s):  
Computational Time ◽  
Indoor Environments ◽  
Laser Range Finder ◽  
Memory Usage ◽  
High Complexity ◽  
Line Segments ◽  
Laser Range ◽  
Localization And Mapping ◽  
Orthogonal Property ◽  
Slam Algorithm

Simultaneous Localization and Mapping (SLAM) is an important technique for robotic system navigation. Due to the high complexity of the algorithm, SLAM usually needs long computational time or large amount of memory to achieve accurate results. In this paper, we present a lightweight Rao-Blackwellized particle filter- (RBPF-) based SLAM algorithm for indoor environments, which uses line segments extracted from the laser range finder as the fundamental map structure so as to reduce the memory usage. Since most major structures of indoor environments are usually orthogonal to each other, we can also efficiently increase the accuracy and reduce the complexity of our algorithm by exploiting this orthogonal property of line segments, that is, we treat line segments that are parallel or perpendicular to each other in a special way when calculating the importance weight of each particle. Experimental results shows that our work is capable of drawing maps in complex indoor environments, needing only very low amount of memory and much less computational time as compared to other grid map-based RBPF SLAM algorithms.

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