Navigation In Indoor Environments Research Articles

SA-MLP-Mixer: A Compact All-MLP Deep Neural Net Architecture for UAV Navigation in Indoor Environments

SA-MLP-Mixer: A Compact All-MLP Deep Neural Net Architecture for UAV Navigation in Indoor Environments

A Low-Cost and Robust Multi-Sensor Data Fusion Scheme for Heterogeneous Multi-Robot Cooperative Positioning in Indoor Environments

The latest development of multi-robot collaborative systems has put forward higher requirements for multi-sensor fusion localization. Current position methods mainly focus on the fusion of the carrier’s own sensor information, and how to fully utilize the information of multiple robots to achieve high-precision positioning is a major challenge. However, due to the comprehensive impact of factors such as poor performance, variety, complex calculations, and accumulation of environmental errors used by commercial robots, the difficulty of high-precision collaborative positioning is further exacerbated. To address this challenge, we propose a low-cost and robust multi-sensor data fusion scheme for heterogeneous multi-robot collaborative navigation in indoor environments, which integrates data from inertial measurement units (IMUs), laser rangefinders, cameras, and so on, into heterogeneous multi-robot navigation. Based on Discrete Kalman Filter (DKF) and Extended Kalman Filter (EKF) principles, a three-step joint filtering model is used to improve the state estimation and the visual data are processed using the YOLO deep learning target detection algorithm before updating the integrated filter. The proposed integration is tested at multiple levels in an open indoor environment following various formation paths. The results show that the three-dimensional root mean square error (RMSE) of indoor cooperative localization is 11.3 mm, the maximum error is less than 21.4 mm, and the motion error in occluded environments is suppressed. The proposed fusion scheme is able to satisfy the localization accuracy requirements for efficient and coordinated motion of autonomous mobile robots.

Unsupervised Visual Odometry and Action Integration for PointGoal Navigation in Indoor Environment

PointGoal navigation in indoor environment is a fundamental task for personal robots to navigate to a specified point. Recent studies solved this PointGoal navigation task with near-perfect success rate in photo-realistically simulated environments, under the assumptions with noiseless actuation and most importantly, perfect localization with GPS and compass sensors. However, accurate GPS signalis difficult to be obtained in real indoor environment. To improve the PointGoal navigation accuracy without GPS signal, we use visual odometry (VO) and propose a novel action integration module (AIM) trained in unsupervised manner. Sepecifically, unsupervised VO computes the relative pose of the agent from the re-projection error of two adjacent frames, and then replaces the accurate GPS signal with the path integration. The pseudo position estimated by VO is used to train action integration which assists agent to update their internal perception of location and helps improve the success rate of navigation. The training and inference process only use RGB, depth, collision as well as self-action information. The experiments show that the proposed system achieves satisfactory results and outperforms the partially supervised learning algorithms on the popular Gibson dataset.

Motion Planning of Mobile Manipulator for Navigation Including Door Traversal

Door traversal is an essential task for a mobile manipulator to achieve autonomous navigation in indoor environment. However, it remains challenging since it requires the coupled motion of the robot and door and is composed of several sub-problems such as approaching, opening, passing through, and closing the door. This paper proposes a planning framework that formulates these sub-problems as a single problem and computes a path for the mobile manipulator to navigate from the initial position, traverse through the door, and arrive at the target position. The proposed framework obtains the path of the whole-body configuration in two steps. First, the path for the pose of the mobile robot and the path for the door angle are computed by using the graph search algorithm. In graph search, an integer variable called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">area indicator</i> is introduced as an element of state, which indicates where the robot is located relative to the door. Especially, the area indicator expresses a process of door traversal. In the second step, the configuration of the manipulator is computed by the Inverse Kinematics (IK) solver from the path of the mobile robot and door angle. The proposed framework has a distinct advantage over the existing methods that manually determine several parameters such as which direction to approach the door and the angle of the door required for passage. The effectiveness of the proposed framework was validated through experiments with a nonholonomic mobile manipulator.

A SLAM-Based Localization and Navigation System for Social Robots: The Pepper Robot Case

Robot navigation in indoor environments has become an essential task for several applications, including situations in which a mobile robot needs to travel independently to a certain location safely and using the shortest path possible. However, indoor robot navigation faces challenges, such as obstacles and a dynamic environment. This paper addresses the problem of social robot navigation in dynamic indoor environments, through developing an efficient SLAM-based localization and navigation system for service robots using the Pepper robot platform. In addition, this paper discusses the issue of developing this system in a way that allows the robot to navigate freely in complex indoor environments and efficiently interact with humans. The developed Pepper-based navigation system has been validated using the Robot Operating System (ROS), an efficient robot platform architecture, in two different indoor environments. The obtained results show an efficient navigation system with an average localization error of 0.51 m and a user acceptability level of 86.1%.

A new spatial database framework for pedestrian indoor navigation based on the OpenStreetMap tag information

AbstractIt is widely acknowledged that the tools for pedestrian navigation in indoor environments have been increasing while the internal spaces of buildings continue to grow in complexity. The majority of mobile applications for indoor navigation are mostly dependent on access to radio‐frequency identification (RFID) and WiFi, which are particularly limited and expensive in most of Sub‐Saharan Africa. In addition, data from Volunteered Geographic Information such as OpenStreetMap (OSM) lacked clear framework and specification for database design for indoor navigation. Accordingly, this study proposed and illustrated a new database framework for indoor navigation by taking advantage of the popular OSM tag information structure. The proposed framework characterized the indoor environment based on horizontal and vertical partitions, together with description of indoor features by using cardinal directions and qualitative descriptions. The framework was demonstrated by creating sample database records, and retrieval of step‐by‐step travel information for different internal configurations of the indoor environment. A key contribution of this framework is its simplistic and low‐cost nature, where user travel information is retrieved from the database with no need for communication signals from Global Positioning Systems, RFID, or WiFi, making it advantageous for low‐cost applications where access to these communication infrastructure are limited.

Detecting and tracking using 2D laser range finders and deep learning

Detecting and tracking people using 2D laser rangefinders (LRFs) is challenging due to the features of the human leg motion, high levels of self-occlusion and the existence of objects which are similar to the human legs. Previous approaches use datasets that are manually labelled with support of images of the scenes. We propose a system with a calibrated monocular camera and 2D LRF mounted on a mobile robot in order to generate a dataset of leg patterns through automatic labelling which is valid to achieve a robust and efficient 2D LRF-based people detector and tracker. First, both images and 2D laser data are recorded during the robot navigation in indoor environments. Second, the people detection boxes and keypoints obtained by a deep learning-based object detector are used to locate both people and their legs on the images. The coordinates frame of 2D laser is extrinsically calibrated to the camera coordinates allowing our system to automatically label the leg instances. The automatically labelled dataset is then used to achieve a leg detector by machine learning techniques. To validate the proposal, the leg detector is used to develop a Kalman filter-based people detection and tracking algorithm which is experimentally assessed. The experimentation shows that the proposed system overcomes the Angus Leigh’s detector and tracker which is considered the state of the art on 2D LRF-based people detector and tracker.

Sparse-Map: automatic topological map creation via unsupervised learning techniques

Most robots use 2D occupancy grid maps for navigation, localization, and path-planning. This model is flexible and allows to represent any geometrical shape with finite accuracy. However, this dense representation imposes high memory requirements and does not generalize well to 3D environments. We present a task-based map compression technique useful for path-planning and navigation in indoor environments for service robots where, from a point cloud of 3D map features, we calculate a number of clusters based on their spatial position and generate a sparse 3D representation of the environment. Moreover, we propose several metrics to assess the quality and performance of a map representation, and we tested our proposal using a series of point-cloud benchmarks and clustering techniques where our method has a comparable performance using a fraction of the memory footprint than the baselines. Finally, we have released our system as a Robot Operating System (ROS) based open source library.

Indoor Visual-Based Localization System for Multi-Rotor UAVs.

Industry 4.0, smart homes, and the Internet of Things are boosting the employment of autonomous aerial vehicles in indoor environments, where localization is still challenging, especially in the case of close and cluttered areas. In this paper, we propose a Visual Inertial Odometry localization method based on fiducial markers. Our approach enables multi-rotor aerial vehicle navigation in indoor environments and tackles the most challenging aspects of image-based indoor localization. In particular, we focus on a proper and continuous pose estimation, working from take-off to landing, at several different flying altitudes. With this aim, we designed a map of fiducial markers that produces results that are both dense and heterogeneous. Narrowly placed tags lead to minimal information loss during rapid aerial movements while four different classes of marker size provide consistency when the camera zooms in or out according to the vehicle distance from the ground. We have validated our approach by comparing the output of the localization algorithm with the ground-truth information collected through an optoelectronic motion capture system, using two different platforms in different flying conditions. The results show that error mean and standard deviation can remain constantly lower than , so not degrading when the aerial vehicle increases its altitude and, therefore, strongly improving similar state-of-the-art solutions.

An Intelligent Location-Based Virtual Indoor Positioning and Navigation Using GPS and Smartphone Sensors

Abstract: Various solutions have been suggested for indoor navigation systems in which most methods require the support of external physical hardware infrastructure which increases the complexity and cost of set-up of supporting hardware requirements, which will always be an issue with such systems. In this paper, we present the design of a smartphone based indoor navigation system. The proposed method is supported by a web based architecture, for easily creating indoor maps and providing an indoor location’s information for navigation and positioning. The system has been implemented and the results indicate that the approach is useful for navigation in indoor environments. Terms: Indoor Positioning, Map Generation, Indoor Navigation.

基于改进EKF算法的电动叉车组合导航定位方法

Aiming at the problem of inaccurate navigation and positioning of electric forklifts in a complex environment with multiple placement racks when carrying stored crops in a warehouse, this paper proposes a combined navigation and positioning system based on information fusion of LiDAR and inertial measurement units. The method proposed in this paper improves the traditional EKF algorithm by introducing factors affecting the prior covariance matrix and changing the weights of processing old and new data in the filtering equation to achieve the desired goal of suppressing system dispersion and to accomplish accurate estimation of the position of electric forklifts in the storage room. The simulation of robot positioning and navigation in indoor environment shows that the improved algorithm improves the position estimation accuracy by about 30% compared with the traditional algorithm, the new algorithm can effectively improve the efficiency of electric forklift for handling and storage, and it can ensure the robustness of robot position estimation.

A CNN-Based System for Mobile Robot Navigation in Indoor Environments via Visual Localization with a Small Dataset

Deep learning has made great advances in the field of image processing, which allows automotive devices to be more widely used in humans’ daily lives than ever before. Nowadays, the mobile robot navigation system is among the hottest topics that researchers are trying to develop by adopting deep learning methods. In this paper, we present a system that allows the mobile robot to localize and navigate autonomously in the accessible areas of an indoor environment. The proposed system exploits the Convolutional Neural Network (CNN) model’s advantage to extract data feature maps for image classification and visual localization, which attempts to precisely determine the location region of the mobile robot focusing on the topological maps of the real environment. The system attempts to precisely determine the location region of the mobile robot by integrating the CNN model and topological map of the robot workspace. A dataset with small numbers of images is acquired from the MYNT EYE camera. Furthermore, we introduce a new loss function to tackle the bounded generalization capability of the CNN model in small datasets. The proposed loss function not only considers the probability of the input data when it is allocated to its true class but also considers the probability of allocating the input data to other classes rather than its actual class. We investigate the capability of the proposed system by evaluating the empirical studies based on provided datasets. The results illustrate that the proposed system outperforms other state-of-the-art techniques in terms of accuracy and generalization capability.

Ultra-Wideband Positioning Sensor with Application to an Autonomous Ultraviolet-C Disinfection Vehicle.

Due to the COVID-19 virus being highly transmittable, frequently cleaning and disinfecting facilities is common guidance in public places. However, the more often the environment is cleaned, the higher the risk of cleaning staff getting infected. Therefore, strong demand for sanitizing areas in automatic modes is undoubtedly expected. In this paper, an autonomous disinfection vehicle with an Ultraviolet-C (UVC) lamp is designed and implemented using an ultra-wideband (UWB) positioning sensor. The UVC dose for 90% inactivation of the reproductive ability of COVID-19 is 41.7 J/m2, which a 40 W UVC lamp can achieve within a 1.6 m distance for an exposure time of 30 s. With this UVC lamp, the disinfection vehicle can effectively sterilize in various scenarios. In addition, the high-accuracy UWB positioning system, with the time difference of arrival (TDOA) algorithm, is also studied for autonomous vehicle navigation in indoor environments. The number of UWB tags that use a synchronization protocol between UWB anchors can be unlimited. Moreover, this proposed Gradient Descent (GD), which uses Taylor method, is a high-efficient algorithm for finding the optimal position for real-time computation due to its low error and short calculating time. The generalized traversal path planning procedure, with the edge searching method, is presented to improve the efficiency of autonomous navigation. The average error of the practical navigation demonstrated in the meeting room is 0.10 m. The scalability of the designed system to different application scenarios is also discussed and experimentally demonstrated. Hence, the usefulness of the proposed UWB sensor applied to UVC disinfection vehicles to prevent COVID-19 infection is verified by employing it to sterilize indoor environments without human operation.

An EKF-based multiple data fusion for mobile robot indoor localization

Purpose Indoor localization is a key tool for robot navigation in indoor environments. Traditionally, robot navigation depends on one sensor to perform autonomous localization. This paper aims to enhance the navigation performance of mobile robots, a multiple data fusion (MDF) method is proposed for indoor environments. Design/methodology/approach Here, multiple sensor data i.e. collected information of inertial measurement unit, odometer and laser radar, are used. Then, an extended Kalman filter (EKF) is used to incorporate these multiple data and the mobile robot can perform autonomous localization according to the proposed EKF-based MDF method in complex indoor environments. Findings The proposed method has experimentally been verified in the different indoor environments, i.e. office, passageway and exhibition hall. Experimental results show that the EKF-based MDF method can achieve the best localization performance and robustness in the process of navigation. Originality/value Indoor localization precision is mostly related to the collected data from multiple sensors. The proposed method can incorporate these collected data reasonably and can guide the mobile robot to perform autonomous navigation (AN) in indoor environments. Therefore, the output of this paper would be used for AN in complex and unknown indoor environments.

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.

L5IN: Overview of an Indoor Navigation Pilot Project

While outdoor navigation systems are already represented everywhere, the enclosed space is much less developed. The project Level 5 Indoor Navigation (L5IN) presents a new approach with mobile phone standard 5G as the orientation signal and without additional infrastructure for navigation in indoor environments. The aim of this project is to use the new available 5G technology to show how navigation systems, which have thus far only been available in the outdoor segment, can now be integrated into existing smartphone systems for indoor navigation. This paper gives an overview of the project and presents the different work packages leading to a holistic approach towards the development of an indoor navigation application for pedestrians. By using a specific app with open interfaces, it is planned to make navigation possible in all buildings modeled according to certain standards. The challenge involved is that, unlike outdoor maps, there is no map basis for buildings. For this reason, different approaches to map generation were examined. In a building information model (BIM), all information will be collected and made available via a database for positioning and visualization. The focus is furthermore on positioning, achieved through smartphone sensors and 5G, so that users can orientate themselves in buildings without having to connect to singular systems. It shall be shown that positioning with a standard deviation of 2–3 m and a confidence interval of 68 % is possible. Another advantage of 5G, the ability to send real-time data in higher data packages, will be used for data transmission. The basic idea of 5G-based indoor navigation will be enabled with radio cells of the providers, which will be set up on the HafenCity University campus. The complex university building will be used as a prototype environment.

Towards the Development of a Brain Semi-controlled Wheelchair for Navigation in Indoor Environments

Several technological advancements emerged providing the technical assistance supporting people with special needs in tackling their everyday tasks. Particularly, with the advancements in cost-effective Brain-Computer Interfaces (BCI), they can be very useful for people with disabilities to improve their quality of life. This paper investigates the usability of low-cost BCI for navigation in an indoor environment, which is considered one of the daily challenges facing individuals with mobility impairment. A software framework is proposed to control a wheelchair using three modes of operations: brain-controlled, autonomous and semi-autonomous, taking into consideration the usability and safety requirements. A prototype system based on the proposed framework was developed. The system can detect an obstacle in the front, right and left sides of the wheelchair and can stop the movement automatically to avoid collation. The usability evaluation of the proposed system, in terms of effectiveness, efficiency and satisfaction, shows that it can be very helpful in the daily life of the mobility impaired people. An experiment was conducted to assess the usability of the proposed framework using the prototype system. Subjects steered the wheelchair using the three different operation modes effectively by controlling the direction of motion.

GTRS-Based Algorithm for UAV Navigation in Indoor Environments Employing Range Measurements and Odometry

This work addresses the problem of unmanned aerial vehicle (UAV) navigation in indoor environments. Due to unavailability of satellite signals, the proposed algorithm takes advantage of terrestrial radio measurements between the UAV and a set of stationary reference points, from which it extracts range information, as well as odometry by means of inertial sensors, such as accelerometer. On the one hand, based on maximum a posteriori (MAP) criterion, the range information and accumulated knowledge throughout the UAV's movement are employed to derive a generalized trust region sub-problem (GTRS), that is solved exactly via bisection procedure. On the other hand, by using the UAV's transform in relation to the world, another position estimation is obtained by employing odometry. Finally, the two position estimates are combined through a Kalman filter (KF) to enhance the positioning accuracy and obtain the final UAV's position estimation. The UAV is then navigated to a desired destination, by simply calculating the velocity components in the shortest path. Our results show that the proposed algorithm is robust to various model parameters for high precision (HP) UAV sensors, achieving reasonably good positioning accuracy. Besides, the results corroborate that the proposed algorithm is suitable for real-time applications, consuming (on average) only 21 ms to estimate the UAV position.

Indoor Navigation Technologies Based on RFID Systems to Assist Visually Impaired People: A Review and a Proposal

This paper presents a study of the main contributions, in terms of the development of electronics and RFID technologies, to assist people with visual disabilities in their mobilization in indoor spaces. The main aspects taken into consideration when designing an assistive system for people with visual impairments for planning and execution of a movement from one place to another and/or for exploration of a site of interest autonomously are included. This study is important to identify the main technologies based on circuits and systems, electronics, ICT, and RF communication that have been proposed to help people with this type of disability in their indoor mobilization, as well as the techniques of location, navigation, orientation, and detection of objects, among other important functionalities for this type of systems. This work allowed us to propose the conceptual design of a novel system based on RFID and electronics technologies to help visually impaired people in their navigation in indoor environments, which will contribute to their social inclusion.

P2PNavi: A System-Level Algorithmic Solution for Highly Accurate Direction Estimation for Infrastructure-Free Indoor Navigation

Peer-to-peer (P2P) direction estimation based on rotary (TOA) ranging is an effective method for infrastructure-free navigation in indoor environments, which is crucial to first responders, especially firefighters. With the sequential measured distances and corresponding azimuth angles, P2P direction estimation can be defined as a sequence matching problem. This article presents P2PNavi, a system-level algorithmic solution, to overcome the impact of unavoidable indoor ranging error on the matching accuracy. By means of novel preprocessing and matching algorithms, P2PNavi can effectively improve the signal-to-noise ratio (SNR) of the measured sequence and reduce the direction estimation error. The solution is verified by experiments with commercially available TOA devices in a typical office building. P2PNavi significantly outperforms nonsystem-level methods in terms of the root mean square error of direction estimation.