Map Of Indoor Environment Research Articles

ViMantic, a distributed robotic architecture for semantic mapping in indoor environments

Semantic maps augment traditional representations of robot workspaces, typically based on their geometry and/or topology, with meta-information about the properties, relations and functionalities of their composing elements. A piece of such information could be: fridges are appliances typically found in kitchens and employed to keep food in good condition. Thereby, semantic maps allow for the execution of high-level robotic tasks in an efficient way, e.g. “Hey robot, Store the leftover salad”. This paper presents ViMantic, a novel semantic mapping architecture for the building and maintenance of such maps, which brings together a number of features as demanded by modern mobile robotic systems, including: (i) a formal model, based on ontologies, which defines the semantics of the problem at hand and establishes mechanisms for its manipulation; (ii) techniques for processing sensory information and automatically populating maps with, for example, objects detected by cutting-edge CNNs; (iii) distributed execution capabilities through a client–server design, making the knowledge in the maps accessible and extendable to other robots/agents; (iv) a user interface that allows for the visualization and interaction with relevant parts of the maps through a virtual environment; (v) public availability, hence being ready to use in robotic platforms. The suitability of ViMantic has been assessed using Robot@Home, a vast repository of data collected by a robot in different houses. The experiments carried out consider different scenarios with one or multiple robots, from where we have extracted satisfactory results regarding automatic population, execution times, and required size in memory of the resultant semantic maps.

Efficient 3D Mapping and Modelling of Indoor Scenes with the Microsoft HoloLens: A Survey

The Microsoft HoloLens is a head-worn mobile augmented reality device. It allows a real-time 3D mapping of its direct environment and a self-localisation within the acquired 3D data. Both aspects are essential for robustly augmenting the local environment around the user with virtual contents and for the robust interaction of the user with virtual objects. Although not primarily designed as an indoor mapping device, the Microsoft HoloLens has a high potential for an efficient and comfortable mapping of both room-scale and building-scale indoor environments. In this paper, we provide a survey on the capabilities of the Microsoft HoloLens (Version 1) for the efficient 3D mapping and modelling of indoor scenes. More specifically, we focus on its capabilities regarding the localisation (in terms of pose estimation) within indoor environments and the spatial mapping of indoor environments. While the Microsoft HoloLens can certainly not compete in providing highly accurate 3D data like laser scanners, we demonstrate that the acquired data provides sufficient accuracy for a subsequent standard rule-based reconstruction of a semantically enriched and topologically correct model of an indoor scene from the acquired data. Furthermore, we provide a discussion with respect to the robustness of standard handcrafted geometric features extracted from data acquired with the Microsoft HoloLens and typically used for a subsequent learning-based semantic segmentation.

Design of Smart Home Service Robot Based on ROS

At present, the functions of home service robots are not perfect, and home service robot systems that can independently complete autonomous inspections and home services are still lacking. In response to this problem, this paper designs a smart home service robot system based on ROS. The system uses Raspberry Pi 3B as the main control to manage the nodes of each sensor. CC2530 sets up a ZigBee network to collect home environmental information and control home electrical appliances. The image information of the home is collected by the USB camera. The human speech is recognized by Baidu Speech Recognition API. When encountering a dangerous situation, the GSM module is used to give users SMS and phone alarms. Arduino mega2560 is used as the bottom controller to control the movement of the service robot. The indoor environment map of the home is constructed by the lidar and the attitude sensor. The service robot finally designed and developed realizes the functions of wireless control of home appliances, voice remote control, autonomous positioning and navigation, liquefied gas leakage alarm, and human infrared detection alarm. Compared with the household service robots in the related literature, the household service robots developed by us have more complete functions. And the robot system has completed the task of combining independent patrol and home service well.

Multi-Robot Indoor Environment Map Building Based on Multi-Stage Optimization Method

For a multi-robot system, the accurate global map building based on a local map obtained by a single robot is an essential issue. The map building process is always divided into three stages: single-robot map acquisition, multi-robot map transmission, and multi-robot map merging. Based on the different stages of map building, this paper proposes a multi-staqe optimization (MSO) method to improve the accuracy of the global map. In the map acquisition stage, we windowed the map based on the position of the robot to obtain the local map. Furthermore, we adopted the extended Kalman filter (EKF) to improve the positioning accuracy, thereby enhancing the accuracy of the map acquisition by the single robot. In the map transmission stage, considering the robustness of the multi-robot system in the real environment, we designed a dynamic self-organized communication topology (DSCT) based on the master and slave sketch to ensure the efficiency and accuracy of map transferring. In the map merging stage, multi-layer information filtering (MLIF) was investigated to increase the accuracy of the global map. We performed simulation experiments on the Gazebo platform and compared the result of the proposed method with that of classic map building methods. In addition, the practicability of this method has been verified on the Turtlebot3 burger robot. Experimental results proved that the MSO method improves the accuracy of the global map built by the multi-robot system.

OFM-SLAM: A Visual Semantic SLAM for Dynamic Indoor Environments

Most of the current visual Simultaneous Localization and Mapping (SLAM) algorithms are designed based on the assumption of a static environment, and their robustness and accuracy in the dynamic environment do not behave well. The reason is that moving objects in the scene will cause the mismatch of features in the pose estimation process, which further affects its positioning and mapping accuracy. In the meantime, the three-dimensional semantic map plays a key role in mobile robot navigation, path planning, and other tasks. In this paper, we present OFM-SLAM: Optical Flow combining MASK-RCNN SLAM, a novel visual SLAM for semantic mapping in dynamic indoor environments. Firstly, we use the Mask-RCNN network to detect potential moving objects which can generate masks of dynamic objects. Secondly, an optical flow method is adopted to detect dynamic feature points. Then, we combine the optical flow method and the MASK-RCNN for full dynamic points’ culling, and the SLAM system is able to track without these dynamic points. Finally, the semantic labels obtained from MASK-RCNN are mapped to the point cloud for generating a three-dimensional semantic map that only contains the static parts of the scenes and their semantic information. We evaluate our system in public TUM datasets. The results of our experiments demonstrate that our system is more effective in dynamic scenarios, and the OFM-SLAM can estimate the camera pose more accurately and acquire a more precise localization in the high dynamic environment.

Dual-Mode Radar Sensor for Indoor Environment Mapping.

In this paper, we introduce mapping results in an indoor environment based on our own developed dual-mode radar sensor. Our radar system uses a frequency-modulated continuous wave (FMCW) with a center frequency of 62 GHz and a multiple-input multiple-output antenna system. In addition, the FMCW radar sensor we designed is capable of dual-mode detection, which alternately transmits two waveforms using different bandwidths within one frame. The first waveform is for long-range detection, and the second waveform is for short-range detection. This radar system is mounted on a small robot that moves in indoor environments such as rooms or hallways, and the radar and the robot send and receive necessary information to each other. The radar estimates the distance, velocity, and angle information of targets around the radar-equipped robot. Then, the radar receives information about the robot’s motion from the robot, such as its speed and rotation angle. Finally, by combining the motion information and the detection results, the radar-equipped robot maps the indoor environment while finding its own position. Compared to the actual map data, the radar-based mapping is effectively achieved through the radar system we developed.

Simultaneous Localization and Mapping using differential drive mobile robot under ROS

The present work shows implementation of SLAM for mapping and navigation in indoor environment under robot operating system ROS, using differential drive robot provided by laser scan. Mapping process is done by using ROS packages based on extended kalman EKF filter algorithm, whereas localization is done by using packages based on adaptive Monte Carlo localization AMCL, navigation is performed by ROS built functions. Paper also will give concrete intuition about using EKF to accomplish SLAM, and shows how the results diverge in case of not using SALM algorithm.

IT-HYFAO-VO: Interpretation Tree-Based VO With Hybrid Feature Association and Optimization

For the visual odometry (VO) and simultaneous localization and mapping (SLAM) in indoor environments, high-level geometric features have attracted more and more attention in recent years. Unlike the generally used point features, the geometric features, such as planes and lines, encode more higher-level semantic information of the scene which is beneficial for various tasks of mobile robots. In this article, an RGB-D VO system with hybrid high-level geometric features is developed. An interpretation tree (IT)-based hybrid feature association framework is proposed, which turns the feature association into a multiple-hypothesis decision problem. The IT expansion method is elaborately designed. Specifically, an internode consistency is proposed for generation of hypotheses and a consistent transformation model (CTM) for each hypothesis is explicitly expressed and incrementally updated. When the IT is constructed, a closed-form solution to the feature association and the camera transformation can be obtained. Then, a hybrid feature joint optimization method is introduced to further refine the pose estimate and parameters of geometric features. During optimization, the planes and lines are appropriately parameterized and the uncertainties arising from feature extraction are derived and used to balance the contributions of two types of features in the cost function. Extensive experiments are executed on public datasets and the results demonstrate that the proposed method can achieve higher accuracy and stronger robustness.

Plane-Edge-SLAM: Seamless Fusion of Planes and Edges for SLAM in Indoor Environments

Planes and edges are attractive features for simultaneous localization and mapping (SLAM) in indoor environments because they can be reliably extracted and are robust to illumination changes. However, it remains a challenging problem to seamlessly fuse two different kinds of features to avoid degeneracy and accurately estimate the camera motion. In this article, a plane-edge-SLAM system using an RGB-D sensor is developed to address the seamless fusion of planes and edges. Constraint analysis is first performed to obtain a quantitative measure of how the planes constrain the camera motion estimation. Then, using the results of the constraint analysis, an adaptive weighting algorithm is elaborately designed to achieve seamless fusion. Through the fusion of planes and edges, the solution to motion estimation is fully constrained, and the problem remains well-posed in all circumstances. In addition, a probabilistic plane fitting algorithm is proposed to fit a plane model to the noisy 3-D points. By exploiting the error model of the depth sensor, the proposed plane fitting is adaptive to various measurement noises corresponding to different depth measurements. As a result, the estimated plane parameters are more accurate and robust to the points with large uncertainties. Compared with the existing plane fitting methods, the proposed method definitely benefits the performance of motion estimation. The results of extensive experiments on public data sets and in real-world indoor scenes demonstrate that the plane-edge-SLAM system can achieve high accuracy and robustness. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the robust localization and mapping for mobile robots. We suggest a novel simultaneous localization and mapping (SLAM) approach fusing the plane and edge features in indoor scenes (plane-edge-SLAM). This newly proposed approach works well in the textureless or dark scenes and is robust to the sensor noise. The experiments are carried out in various indoor scenes for mobile robots, and the results demonstrate the robustness and effectiveness of the proposed framework. In future work, we will address the fusion of other high-level features (for example, 3-D lines) and the active exploration of the environments.

3D Semantic VSLAM of Indoor Environment Based on Mask Scoring RCNN

In view of existing Visual SLAM (VSLAM) algorithms when constructing semantic map of indoor environment, there are problems with low accuracy and low label classification accuracy when feature points are sparse. This paper proposed a 3D semantic VSLAM algorithm called BMASK-RCNN based on Mask Scoring RCNN. Firstly, feature points of images are extracted by Binary Robust Invariant Scalable Keypoints (BRISK) algorithm. Secondly, map points of reference key frame are projected to current frame for feature matching and pose estimation, and an inverse depth filter is used to estimate scene depth of created key frame to obtain camera pose changes. In order to achieve object detection and semantic segmentation for both static objects and dynamic objects in indoor environments and then construct dense 3D semantic map with VSLAM algorithm, a Mask Scoring RCNN is used to adjust its structure partially, where a TUM RGB-D SLAM dataset for transfer learning is employed. Semantic information of independent targets in scenes provides semantic information including categories, which not only provides high accuracy of localization but also realizes the probability update of semantic estimation by marking movable objects, thereby reducing the impact of moving objects on real-time mapping. Through simulation and actual experimental comparison with other three algorithms, results show the proposed algorithm has better robustness, and semantic information used in 3D semantic mapping can be accurately obtained.

Indoor and Outdoor Low-Cost Seamless Integrated Navigation System Based on the Integration of INS/GNSS/LIDAR System

Global Navigation Satellite System (GNSS) provides accurate positioning data for vehicular navigation in open outdoor environment. In an indoor environment, Light Detection and Ranging (LIDAR) Simultaneous Localization and Mapping (SLAM) establishes a two-dimensional map and provides positioning data. However, LIDAR can only provide relative positioning data and it cannot directly provide the latitude and longitude of the current position. As a consequence, GNSS/Inertial Navigation System (INS) integrated navigation could be employed in outdoors, while the indoors part makes use of INS/LIDAR integrated navigation and the corresponding switching navigation will make the indoor and outdoor positioning consistent. In addition, when the vehicle enters the garage, the GNSS signal will be blurred for a while and then disappeared. Ambiguous GNSS satellite signals will lead to the continuous distortion or overall drift of the positioning trajectory in the indoor condition. Therefore, an INS/LIDAR seamless integrated navigation algorithm and a switching algorithm based on vehicle navigation system are designed. According to the experimental data, the positioning accuracy of the INS/LIDAR navigation algorithm in the simulated environmental experiment is 50% higher than that of the Dead Reckoning (DR) algorithm. Besides, the switching algorithm developed based on the INS/LIDAR integrated navigation algorithm can achieve 80% success rate in navigation mode switching.

REINFORCEMENT LEARNING HELPS SLAM: LEARNING TO BUILD MAPS

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.

A COMPLETE FOS APPROACH FOR INDOOR CROWDSOURCED MAPPING: CASE STUDY ON SAPIENZA UNIVERSITY OF ROME FACULTIES

Abstract. Indoor mapping is an essential process in several applications such as the visualization of space and its utilization, security and resource planning, emergency planning and location-based alerts and, last but not least, indoor navigation. In this work, a completely free and open-source (FOS) approach to map indoor environments, and to navigate through them, is presented. Our tests were carried out within Sapienza University of Rome public buildings; in detail, Letters and Philosophy faculty and Engineering faculty indoor environments were mapped. To reach this goal, only open source software such as Quantum GIS (QGIS) and open-source platforms like Open Street Map (OSM) and its indoor viewer, Open Level Up (OLU) were adopted. A database of indoor environments of the two faculties, completely compatible with OLU, was created through QGIS. In this way, a public territorial information system of classrooms, offices and laboratories is accessible to everyone who can, hence, add or modify the information, following the principle of crowdsourcing and of Volunteered Geographic Information (VGI). The developed procedure is now standard and its outputs accepted by the OSM community. Hence, the long-term developments of this project are the proposal for the volunteered and cooperative indoor mapping and design of strategic buildings and infrastructures (hospitals, schools, public offices, shopping centers, stations, airports etc.), starting from the available information (indoor layouts) and knowledge acquired through experience of people who normally work inside them and/or visit them frequently. In this context it is possible to state that the development of VGI for internal maps for strategic buildings, infrastructures and denied GNSS environments, not only supports and improves internal and external navigation without interruption, but can also have a significant positive impact on security and emergency management.

RGB-D camera calibration and trajectory estimation for indoor mapping

In this paper, we present a system for estimating the trajectory of a moving RGB-D camera with applications to building maps of large indoor environments. Unlike the current most researches, we propose a ‘feature model’ based RGB-D visual odometry system for a computationally-constrained mobile platform, where the ‘feature model’ is persistent and dynamically updated from new observations using a Kalman filter. In this paper, we firstly propose a mixture of Gaussians model for the depth random noise estimation, which is used to describe the spatial uncertainty of the feature point cloud. Besides, we also introduce a general depth calibration method to remove systematic errors in the depth readings of the RGB-D camera. We provide comprehensive theoretical and experimental analysis to demonstrate that our model based iterative-closest-point (ICP) algorithm can achieve much higher localization accuracy compared to the conventional ICP. The visual odometry runs at frequencies of 30 Hz or higher, on VGA images, in a single thread on a desktop CPU with no GPU acceleration required. Finally, we examine the problem of place recognition from RGB-D images, in order to form a pose-graph SLAM approach to refining the trajectory and closing loops. We evaluate the effectiveness of the system on using publicly available datasets with ground-truth data. The entire system is available for free and open-source online.

Ultrasonic Radar System Arduino for a Light Free Mapping of Indoor Environment

Ultrasonic Radar System Arduino for a Light Free Mapping of Indoor Environment

A Novel Navigation Algorithm for Mapping Indoor Environments with a Quadrotor

I n the last decade, unmanned aerial vehicle gained popularity and started to be used in different tasks most of which are performed in outdoor environments. Still, there is a great potential to use quadrotors in indoor tasks such as urban relief and disaster operations. In this paper, we developed a framework and a novel target-based navigation algorithm for mapping of an unknown 2D environment with a quadrotor using an ultrawideband system. The target-based navigation algorithm aims to explore map of the environment by moving the border between the discovered and undiscovered areas. It uses A* search algorithm for path planning if there is an obstacle present in the environment. The target-based navigation algorithm is implemented on Gazebo simulator and its performance is compared with the well-known wall following algorithm and exploration algorithm in terms of task completion time and distance travelled. The target-based navigation algorithm outperforms the other two algorithms especially in environments with obstacles

Mobile robot for radiation mapping in indoor environment

Radiation mapping is the process of measuring radiation intensity level at distributed sampling points in a predefined region of interest (ROI). This procedure is crucial in any radiological related emergency to locate contamination hotspot(s) or in routine inspections in any radiation facilities. When workers knowingly enter a high radiation zone to perform radiation mapping, they are exposed to the risk of radiation exposure. Mobile robot is a potential solution that could eliminate the health and safety risk and subsequently improve the measurement accuracy. This paper reports implementation of mobile robot for radiation mapping by using a commercial mobile robot platform, Turtlebot2 in conjunction with Robot Operating System (ROS). Mobile robot is capable to generate physical map of the targeted environment by using Simultaneous Localization and Mapping (SLAM). Simultaneously, Geiger Muller detector is utilized to perform radiation measurement. The radiation data is directly plotted on the physical map hence the radiation intensity distribution throughout the ROI could be assessed. Functionality of the radiation mapping robot is evaluated in real-world experiments and the results will be presented in the paper.

MapperBot/iSCAN: open-source integrated robotic platform and algorithm for 2D mapping

Simultaneous localization and mapping (SLAM) of an unknown environment is a primary requirement for any autonomous robot. In this paper we present a cost-effective, robust, and integrated software/hardware SLAM solution for 2D mapping of large indoor environments. The presented SLAM solution consists of a robotic platform, referred as MapperBot, and an effective implementation of SLAM algorithm, referred as iSCAN. MapperBot is built using commercial of-the-shelf components with an open-source software/hardware design presented in this paper. The SLAM algorithm iSCAN is based on a highly robust laser-scan matching technique, incorporating Rao-Blackwellized particle filter as a mean to increase its accuracy. As a result, a minimal number of particles is required, and the computer processing time is dramatically reduced. MapperBot/iSCAN solution is fully integrated with the Matlab environment, thus providing researchers, students and developers a quick entry into the realm of the SLAM.

Evaluation of HoloLens Tracking and Depth Sensing for Indoor Mapping Applications.

The Microsoft HoloLens is a head-worn mobile augmented reality device that is capable of mapping its direct environment in real-time as triangle meshes and localize itself within these three-dimensional meshes simultaneously. The device is equipped with a variety of sensors including four tracking cameras and a time-of-flight (ToF) range camera. Sensor images and their poses estimated by the built-in tracking system can be accessed by the user. This makes the HoloLens potentially interesting as an indoor mapping device. In this paper, we introduce the different sensors of the device and evaluate the complete system in respect of the task of mapping indoor environments. The overall quality of such a system depends mainly on the quality of the depth sensor together with its associated pose derived from the tracking system. For this purpose, we first evaluate the performance of the HoloLens depth sensor and its tracking system separately. Finally, we evaluate the overall system regarding its capability for mapping multi-room environments.

A Multi-scale Embossed Map Authoring Tool for Indoor Environments.

We introduce a multi-scale embossed map authoring tool (M-EMAT) that produces tactile maps of indoor environments from the building's structural layout and its 3D-scanned interiors on demand. Our tool renders indoor tactile maps at different spatial scales, representing a building's structure, a zoomed-in of a specific area, or an interior of a room. M-EMAT is very easy to use and produces accurate results even in the case of complex building layouts.