Localization and Mapping for Service Robots: Bearing-Only SLAM with an Omnicam

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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Object-Oriented 3D Semantic Mapping Based on Instance Segmentation

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2019.p0695 ◽

2019 ◽

Vol 23 (4) ◽

pp. 695-704 ◽

Cited By ~ 1

Author(s):

Jinxin Chi ◽

◽

Hao Wu ◽

Guohui Tian

Keyword(s):

Object Recognition ◽

Object Oriented ◽

Mapping Method ◽

Service Robots ◽

Semantic Mapping ◽

Continuous Image ◽

Cloud Models ◽

Localization And Mapping ◽

Slam Algorithm ◽

Instance Segmentation

Service robots gain both geometric and semantic information about the environment with the help of semantic mapping, providing more intelligent services. However, a majority of studies for semantic mapping thus far require priori knowledge 3D object models or maps with a few object categories that neglect separate individual objects. In view of these problems, an object-oriented 3D semantic mapping method is proposed by combining state-of-the-art deep-learning-based instance segmentation and a visual simultaneous localization and mapping (SLAM) algorithm, which helps robots not only gain navigation-oriented geometric information about the surrounding environment, but also obtain individually-oriented attribute and location information about the objects. Meanwhile, an object recognition and target association algorithm applied to continuous image frames is proposed by combining visual SLAM, which uses visual consistency between image frames to promote the result of object matching and recognition over continuous image frames, and improve the object recognition accuracy. Finally, a 3D semantic mapping system is implemented based on Mask R-CNN and ORB-SLAM2 frameworks. A simulation experiment is carried out on the ICL-NUIM dataset and the experimental results show that the system can generally recognize all the types of objects in the scene and generate fine point cloud models of these objects, which verifies the effectiveness of our algorithm.

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A real-time semantic visual SLAM approach with points and objects

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420905443 ◽

2020 ◽

Vol 17 (1) ◽

pp. 172988142090544

Author(s):

Peiyu Guan ◽

Zhiqiang Cao ◽

Erkui Chen ◽

Shuang Liang ◽

Min Tan ◽

...

Keyword(s):

Real Time ◽

Object Segmentation ◽

Bundle Adjustment ◽

Service Robots ◽

Object A ◽

Accurate Localization ◽

Localization And Mapping ◽

Point Object ◽

Environment Perception ◽

Object Association

Visual simultaneously localization and mapping (SLAM) is important for self-localization and environment perception of service robots, where semantic SLAM can provide a more accurate localization result and a map with abundant semantic information. In this article, we propose a real-time PO-SLAM approach with the combination of both point and object measurements. With point–point association in ORB-SLAM2, we also consider point–object association based on object segmentation and object–object association, where the object segmentation is employed by combining object detection with depth histogram. Also, besides the constraint of feature points belonging to an object, a semantic constraint of relative position invariance among objects is introduced. Accordingly, two semantic loss functions with point and object information are designed and added to the bundle adjustment optimization. The effectiveness of the proposed approach is verified by experiments.

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Semi-automatically simultaneous localization and mapping for home service robots

2015 International Conference on Advanced Robotics and Intelligent Systems (ARIS) ◽

10.1109/aris.2015.7158374 ◽

2015 ◽

Author(s):

Chung-Lin Lee ◽

Hsiang-Ting Chen ◽

Yi-Bin Lin ◽

Wei-Hsin Yen ◽

Tzuu-Hseng S. Li

Keyword(s):

Simultaneous Localization And Mapping ◽

Service Robots ◽

Localization And Mapping ◽

Home Service

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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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SLAM for Humanoid Multi-Robot Active Cooperation Based on Relative Observation

Sustainability ◽

10.3390/su10082946 ◽

2018 ◽

Vol 10 (8) ◽

pp. 2946

Author(s):

Zhaoyi Pei ◽

Songhao Piao ◽

Mohammed Souidi ◽

Muhammad Qadir ◽

Guo Li

Keyword(s):

Closed Loop ◽

Image Features ◽

Bundle Adjustment ◽

Service Robots ◽

Global Level ◽

Complex Environment ◽

Localization And Mapping ◽

Fundamental Research ◽

Independent Mode ◽

Multi Robot

The simultaneous localization and mapping (SLAM) of robot in the complex environment is a fundamental research topic for service robots. This paper presents a new humanoid multi-robot SLAM mechanism that allows robots to collaborate and localize each other in their own SLAM process. Each robot has two switchable modes: independent mode and collaborative mode. Each robot can respond to the requests of other robots and participate in chained localization of the target robot under the leadership of the organiser. We aslo discuss how to find the solution of optimal strategy for chained localization. This mechanism can improve the performance of bundle adjustment at the global level, especially when the image features are few or the results of closed loop are not ideal. The simulation results show that this method has a great effect on improving the accuracy of multi-robot localization and the efficiency of 3D mapping.

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A new technique in mobile robot simultaneous localization and mapping

Sba Controle & Automação Sociedade Brasileira de Automatica ◽

10.1590/s0103-17592006000200007 ◽

2006 ◽

Vol 17 (2) ◽

pp. 189-204 ◽

Cited By ~ 1

Author(s):

Vivek Anand Sujan ◽

Marco Antonio Meggiolaro ◽

Felipe Augusto Weilemann Belo

Keyword(s):

Mobile Robot ◽

Geometric Model ◽

A Priori ◽

Graph Model ◽

Visual Exploration ◽

Service Robots ◽

Indoor Environments ◽

Imaging Evaluation ◽

Localization And Mapping ◽

Tracking Process

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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