A new technique in mobile robot simultaneous localization and mapping

In field or indoor environments it is usually not possible to provide service robots with detailed a priori environment and task models. In such environments, robots will need to create a dimensionally accurate geometric model by moving around and scanning the surroundings with their sensors, while minimizing the complexity of the required sensing hardware. In this work, an iterative algorithm is proposed to plan the visual exploration strategy of service robots, enabling them to efficiently build a graph model of their environment without the need of costly sensors. In this algorithm, the information content present in sub-regions of a 2-D panoramic image of the environment is determined from the robot's current location using a single camera fixed on the mobile robot. Using a metric based on Shannon's information theory, the algorithm determines, from the 2-D image, potential locations of nodes from which to further image the environment. Using a feature tracking process, the algorithm helps navigate the robot to each new node, where the imaging process is repeated. A Mellin transform and tracking process is used to guide the robot back to a previous node. This imaging, evaluation, branching and retracing its steps continues until the robot has mapped the environment to a pre-specified level of detail. The effectiveness of this algorithm is verified experimentally through the exploration of an indoor environment by a single mobile robot agent using a limited sensor suite.

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Automatically Annotated Mapping for Indoor Mobile Robot Applications

Volume 4: 36th Mechanisms and Robotics Conference, Parts A and B ◽

10.1115/detc2012-71351 ◽

2012 ◽

Author(s):

Ali Gürcan Özkil ◽

Thomas Howard

Keyword(s):

Mobile Robot ◽

Practical Method ◽

Service Robots ◽

Service Robot ◽

Mobile Service ◽

Indoor Environments ◽

And Topology ◽

Localization And Mapping ◽

Indoor Mobile Robot ◽

Mobile Service Robots

This paper presents a new and practical method for mapping and annotating indoor environments for mobile robot use. The method makes use of 2D occupancy grid maps for metric representation, and topology maps to indicate the connectivity of the ‘places-of-interests’ in the environment. Novel use of 2D visual tags allows encoding information physically at places-of-interest. Moreover, using physical characteristics of the visual tags (i.e. paper size) is exploited to recover relative poses of the tags in the environment using a simple camera. This method extends tag encoding to simultaneous localization and mapping in topology space, and fuses camera and robot pose estimations to build an automatically annotated global topo-metric map. It is developed as a framework for a hospital service robot and tested in a real hospital. Experiments show that the method is capable of producing globally consistent, automatically annotated hybrid metric-topological maps that is needed by mobile service robots.

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REINFORCEMENT LEARNING HELPS SLAM: LEARNING TO BUILD MAPS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b4-2020-329-2020 ◽

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.

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A survey of methods for mobile robot localization and mapping in dynamic indoor environments

2018 Conference on Signal Processing And Communication Engineering Systems (SPACES) ◽

10.1109/spaces.2018.8316333 ◽

2018 ◽

Cited By ~ 4

Author(s):

Aishwarya A. Panchpor ◽

Sam Shue ◽

James M. Conrad

Keyword(s):

Mobile Robot ◽

Indoor Environments ◽

Robot Localization ◽

Localization And Mapping ◽

Mobile Robot Localization

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A PHD-SLAM Method for Mixed Birth Map Information Based on Amplitude Information

Mathematical Problems in Engineering ◽

10.1155/2021/8420814 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Fei Zhang ◽

Zijing Zhang ◽

Luxi Yang ◽

Xinyu Zhang

Keyword(s):

Prior Information ◽

Likelihood Function ◽

Signal To Noise Ratio ◽

A Priori ◽

Estimation Accuracy ◽

Indoor Environments ◽

Signal To Noise ◽

Probability Hypothesis Density ◽

Localization And Mapping ◽

Noise Ratio

The Simultaneous Localization and Mapping (SLAM) method of mobile robots has the problem of low accuracy in complex environments with dense clutter and various map features, such as complex indoor environments and underwater environments. This problem is mainly embodied in estimating the location and number of feature points on the map and the position of the robot itself. In order to solve this problem, a new method based on the probability hypothesis density (PHD) SLAM is proposed in this paper, a PHD-SLAM Method for Mixed Birth Map Information Based on Amplitude Information (AI-MBMI-PHD-SLAM). Firstly, this paper proposes a PHD-SLAM method based on amplitude information (AI-PHD-SLAM). The method uses the amplitude information of map features to obtain more precise map features. Then, the clutter likelihood function is used to improve the estimation accuracy of the feature map in the SLAM process. Meanwhile, this paper studies the performance of the PHD-SLAM method with the amplitude information under the condition of the known signal-to-noise ratio or the unknown signal-to-noise ratio. Secondly, aiming at the problem that PHD-SLAM lacks a priori information in the prediction stage, an AI-PHD-SLAM-based mixed birth map information method is added. In this method, map information that has been detected before the previous moment is added to the observation information in the map prediction phase as a new map information set in the prediction phase. This can increase the prior information and improve the problem of insufficient prior information in the prediction stage. The results of the experiments show that the proposed method and the improved method outperform the RB-PHD-SLAM method in estimating the number and location accuracy of map features and have higher computational efficiency.

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Efficiently Learning Metric and Topological Maps with Autonomous Service Robots (Effizientes Lernen metrischer und topologischer Karten mit autonomen Servicerobotern)

it - Information Technology ◽

10.1524/itit.2007.49.4.232 ◽

2007 ◽

Vol 49 (4) ◽

Cited By ~ 2

Author(s):

Cyrill Stachniss ◽

Giorgio Grisetti ◽

Oscar Martinez Mozos ◽

Wolfram Burgard

Keyword(s):

Mobile Robot ◽

Semantic Information ◽

Machine Learning Techniques ◽

Service Robots ◽

Range Data ◽

Topological Map ◽

Topological Maps ◽

Localization And Mapping ◽

Learning Techniques ◽

Wide Range

SummaryModels of the environment are needed for a wide range of robotic applications, from search and rescue to automated vacuum cleaning. Learning maps has therefore been a major research focus in the robotics community over the last decades. In general, one distinguishes between metric and topological maps. Metric maps model the environment based on grids or geometric representations whereas topological maps model the structure of the environment using a graph. The contribution of this paper is an approach that learns a metric as well as a topological map based on laser range data obtained with a mobile robot. Our approach consists of two steps. First, the robot solves the simultaneous localization and mapping problem using an efficient probabilistic filtering technique. In a second step, it acquires semantic information about the environment using machine learning techniques. This semantic information allows the robot to distinguish between different types of places like, e. g., corridors or rooms. This enables the robot to construct annotated metric as well as topological maps of the environment. All techniques have been implemented and thoroughly tested using real mobile robot in a variety of environments.

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Simultaneous Localization and Mapping of a Mobile Robot using a Kinect v2 Sensor in Indoor Environments

10.26678/abcm.cobem2017.cob17-0296 ◽

2017 ◽

Author(s):

João Carlos Virgolino Soares ◽

Marco Antonio Meggiolaro

Keyword(s):

Mobile Robot ◽

Simultaneous Localization And Mapping ◽

Indoor Environments ◽

Kinect V2 ◽

Localization And Mapping

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Risk-Informed-RRT*: A Sampling-based Human-friendly Motion Planning Algorithm for Mobile Service Robots in Indoor Environments

2018 IEEE International Conference on Information and Automation (ICIA) ◽

10.1109/icinfa.2018.8812396 ◽

2018 ◽

Author(s):

Wenzheng Chi ◽

Jiankun Wang ◽

Max Qing-Hu Meng

Keyword(s):

Motion Planning ◽

Service Robots ◽

Mobile Service ◽

Indoor Environments ◽

Planning Algorithm ◽

Mobile Service Robots

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A Multi-Feature Fusion Slam System Attaching Semantic Invariant to Points and Lines

Sensors ◽

10.3390/s21041196 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1196

Author(s):

Gang Li ◽

Yawen Zeng ◽

Huilan Huang ◽

Shaojian Song ◽

Bin Liu ◽

...

Keyword(s):

Feature Matching ◽

Feature Fusion ◽

Error Function ◽

Line Segments ◽

Cumulative Error ◽

Localization And Mapping ◽

Tracking Process ◽

Line Features ◽

Point Line ◽

Segment Data

The traditional simultaneous localization and mapping (SLAM) system uses static points of the environment as features for real-time localization and mapping. When there are few available point features, the system is difficult to implement. A feasible solution is to introduce line features. In complex scenarios containing rich line segments, the description of line segments is not strongly differentiated, which can lead to incorrect association of line segment data, thus introducing errors into the system and aggravating the cumulative error of the system. To address this problem, a point-line stereo visual SLAM system incorporating semantic invariants is proposed in this paper. This system improves the accuracy of line feature matching by fusing line features with image semantic invariant information. When defining the error function, the semantic invariant is fused with the reprojection error function, and the semantic constraint is applied to reduce the cumulative error of the poses in the long-term tracking process. Experiments on the Office sequence of the TartanAir dataset and the KITTI dataset show that this system improves the matching accuracy of line features and suppresses the cumulative error of the SLAM system to some extent, and the mean relative pose error (RPE) is 1.38 and 0.0593 m, respectively.

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