Unknown Indoor Environment Research Articles

A lightweight autonomous MAV for indoor search and rescue

AbstractMicro Aerial Vehicles (MAVs) have great potentials to be applied for indoor search and rescue missions. In this paper, we propose a modular lightweight design of an autonomous MAV with integrated hardware and software. The MAV is equipped with the 2D laser scanner, camera, mission computer and flight controller, running all the computation onboard in real time. The onboard perception system includes a laser‐based SLAM module and a custom‐designed visual detection module. A dual Kalman filter design provides robust state estimation by multiple sensor fusion. Specifically, the fusion module provides robust altitude measurement in the circumstance of surface changing. In addition, indoor‐outdoor transition is explicitly handled by the fusion module. In order to efficiently navigate through obstacles and adapt to multiple tasks, a task tree‐based mission planning method is seamlessly integrated with path planning and control modules. The MAV is capable of searching and rescuing victims from unknown indoor environments effectively. It was validated by our award‐winning performance at the 2017 International Micro Air Vehicle Competition (IMAV 2017), held in Toulouse, France. The performance video is available on https://youtu.be/8H19ppS_VXM.

A SCENE-ASSISTED POINT-LINE FEATURE BASED VISUAL SLAM METHOD FOR AUTONOMOUS FLIGHT IN UNKNOWN INDOOR ENVIRONMENTS

Abstract. Since Global Navigation Satellite System may be unavailable in complex dynamic environments, visual SLAM systems have gained importance in robotics and its applications in recent years. The SLAM system based on point feature tracking shows strong robustness in many scenarios. Nevertheless, point features over images might be limited in quantity or not well distributed in low-textured scenes, which makes the behaviour of these approaches deteriorate. Compared with point features, line features as higher-dimensional features can provide more environmental information in complex scenes. As a matter of fact, line segments are usually sufficient in any human-made environment, which suggests that scene characteristics remarkably affect the performance of point-line feature based visual SLAM systems. Therefore, this paper develops a scene-assisted point-line feature based visual SLAM method for autonomous flight in unknown indoor environments. First, ORB point features and Line Segment Detector (LSD)-based line features are extracted and matched respectively to build two types of projection models. Second, in order to effectively combine point and line features, a Convolutional Neural Network (CNN)-based model is pre-trained based on the scene characteristics for weighting their associated projection errors. Finally, camera motion is estimated through non-linear minimization of the weighted projection errors between the correspondent observed features and those projected from previous frames. To evaluate the performance of the proposed method, experiments were conducted on the public EuRoc dataset. Experimental results indicate that the proposed method outperforms the conventional point-line feature based visual SLAM method in localization accuracy, especially in low-textured scenes.

Vision-Based Guidance and Switching-Based Sliding Mode Controller for a Mobile Robot in the Cyber Physical Framework

This paper proposes a vision-based guidance strategy for safe navigation of a nonholonomic mobile robot in unknown indoor environments. The proposed switching-based sliding mode control (SMC) law makes the robot follow the desired trajectory as given by the guidance law. The guidance strategy uses the centroid of the depth map of an obstacle as obtained from the Red Green Blue -Depth (RGB-D) sensor to generate the desired angular velocity. The fuzzy rule-based guidance is developed to generate the desired linear velocity command. The analysis of guidance strategy is done for an infinite length obstacle. The proposed SMC is shown to be asymptotically stable using Krasovskii Method. The finite time convergence of robot navigation has been shown using Poincare Map method. The stability of the proposed SMC under burst losses has also been established. Experiments on the Pioneer P3-DX robot in different obstacle scenarios show that the robot safely navigates in presence of communication channel burst losses.

Cooperative Path Planning for Multiple Mobile Robots via HAFSA and an Expansion Logic Strategy

The cooperative path planning problem of multiple mobile robots in an unknown indoor environment is considered in this article. We presented a novel obstacle avoidance and real-time navigation algorithm. The proposed approach consisted of global path planning and local path planning via HAFSA (hybrid artificial fish swarm algorithm) and an expansion logic strategy. Meanwhile, a kind of scoring function was developed, which shortened the time of local path planning and improved the decision-making ability of the path planning algorithm. Finally, using STDR (simple two dimensional robot simulator) and RVIZ (robot operating system visualizer), a multiple mobile robot simulation platform was designed to verify the presented real-time navigation algorithm. Simulation experiments were performed to validate the effectiveness of the proposed path planning method for multiple mobile robots.

An Indoor and Outdoor Navigation System for Visually Impaired People

In this paper, we present a system that allows visually impaired people to autonomously navigate in an unknown indoor and outdoor environment. The system, explicitly designed for low vision people, can be generalized to other users in an easy way. We assume that special landmarks are posed for helping the users in the localization of pre-defined paths. Our novel approach exploits the use of both the inertial sensors and the camera integrated into the smartphone as sensors. Such a navigation system can also provide direction estimates to the tracking system to the users. The success of out approach is proved both through experimental tests performed in controlled indoor environments and in real outdoor installations. A comparison with deep learning methods has been presented.

Enhanced Autonomous Exploration and Mapping of an Unknown Environment with the Fusion of Dual RGB-D Sensors

Abstract The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate two-dimensional (2D) grid maps. Red/green/blue-depth (RGB-D) sensors provide both color and depth information on the environment, thereby enabling the generation of a three-dimensional (3D) point cloud map that is intuitive for human perception. In this paper, we present a systematic approach with dual RGB-D sensors to achieve the autonomous exploration and mapping of an unknown indoor environment. With the synchronized and processed RGB-D data, location points were generated and a 3D point cloud map and 2D grid map were incrementally built. Next, the exploration was modeled as a partially observable Markov decision process. Partial map simulation and global frontier search methods were combined for autonomous exploration, and dynamic action constraints were utilized in motion control. In this way, the local optimum can be avoided and the exploration efficacy can be ensured. Experiments with single connected and multi-branched regions demonstrated the high robustness, efficiency, and superiority of the developed system and methods.

An Eight-Direction Scanning Detection Algorithm for the Mapping Robot Pathfinding in Unknown Indoor Environment.

Aiming at the problem of how to enable the mobile robot to navigate and traverse efficiently and safely in the unknown indoor environment and map the environment, an eight-direction scanning detection (eDSD) algorithm is proposed as a new pathfinding algorithm. Firstly, we use a laser-based SLAM (Simultaneous Localization and Mapping) algorithm to perform simultaneous localization and mapping to acquire the environment information around the robot. Then, according to the proposed algorithm, the 8 certain areas around the 8 directions which are developed from the robot’s center point are analyzed in order to calculate the probabilistic path vector of each area. Considering the requirements of efficient traverse and obstacle avoidance in practical applications, the proposal can find the optimal local path in a short time. In addition to local pathfinding, the global pathfinding is also introduced for unknown environments of large-scale and complex structures to reduce the repeated traverse. The field experiments in three typical indoor environments demonstrate that deviation of the planned path from the ideal path can be kept to a low level in terms of the path length and total time consumption. It is confirmed that the proposed algorithm is highly adaptable and practical in various indoor environments.

Fast Signals of Opportunity Fingerprint Database Maintenance with Autonomous Unmanned Ground Vehicle for Indoor Positioning.

Indoor positioning technology based on Received Signal Strength Indicator (RSSI) fingerprints is a potential navigation solution, which has the advantages of simple implementation, low cost and high precision. However, as the radio frequency signals can be easily affected by the environmental change during its transmission, it is quite necessary to build location fingerprint database in advance and update it frequently, thereby guaranteeing the positioning accuracy. At present, the fingerprint database building methods mainly include point collection and line acquisition, both of which are usually labor-intensive and time consuming, especially in a large map area. This paper proposes a fast and efficient location fingerprint database construction and updating method based on a self-developed Unmanned Ground Vehicle (UGV) platform NAVIS, called Automatic Robot Line Collection. A smartphone was installed on NAVIS for collecting indoor Received Signal Strength Indicator (RSSI) fingerprints of Signals of Opportunity (SOP), such as Bluetooth and Wi-Fi. Meanwhile, indoor map was created by 2D LiDAR-based Simultaneous Localization and Mapping (SLAM) technology. The UGV automatically traverse the unknown indoor environment due to a pre-designed full-coverage path planning algorithm. Then, SOP sensors collect location fingerprints and generates grid map during the process of environment-traversing. Finally, location fingerprint database is built or updated by Kriging interpolation. Field tests were carried out to verify the effectiveness and efficiency of our proposed method. The results showed that, compared with the traditional point collection and line collection schemes, the root mean square error of the fingerprinting-based positioning results were reduced by 35.9% and 25.0% in static tests and 30.0% and 21.3% respectively in dynamic tests. Moreover, our UGV can traverse the indoor environment autonomously without human-labor on data acquisition, the efficiency of the automatic robot line collection scheme is 2.65 times and 1.72 times that of the traditional point collection and the traditional line acquisition, respectively.

Autonomous Point Cloud Acquisition of Unknown Indoor Scenes

This paper presents a methodology for the automatic selection of heuristic scanning positions in unknown indoor environments. The surveying is carried out by a robotic system following a stop-and-go procedure. Starting with a random scan position in the room, the point cloud is discretized in voxels and they are submitted to a two-step classification and are labelled as occupied, occluded, empty, window, door, or exterior based on a visibility analysis. The main objective of the methodology is to obtain a complete point cloud of the indoor space and accordingly, the next best position is the scan position minimizing occluded voxels. Because the method locates doors and windows, the room can be delimited and the scan can continue for adjacent rooms. This approach has been tested in a real case study, in which three scans were developed.

Visual Servoing on the Generalized Voronoi Diagram Using an Omnidirectional Camera

The Generalized Voronoi Diagram (GVD) is a powerful environment representation, since, among other reasons, it defines a set of paths at maximal distance from the obstacles. Many works implicitly use this property to define safe navigation strategies for a mobile robot, but, in practice, only a few explicitly extract in real time the GVD from its perception to induce motion. This article addresses this challenge for a mobile robot solely equipped with an omnidirectional camera, and proposes an autonomous navigation strategy in unknown indoor and/or outdoor environments. The problem is formulated as a visual servoing task, and uses an anisotropic skeletonization algorithm to identify the projection in the image of the local GVD, while suppressing unsafe navigable paths thanks to a context-defined pruning parameter. Unlike rangefinder based system, the use of an omnidirectional camera allows to efficiently deal with outdoor scenes, since the navigable space is extracted using photometric cues. The servoing is then performed using a local linear approximation of the GVD, defined by a conic extracted in real time in the omnidirectional image. To assert the relevancy and efficiency of the proposed approach, extended experimental results are presented, both on indoor and outdoor scenarios.

The development of an autonomous navigation system with optimal control of an UAV in partly unknown indoor environment

This paper presents an autonomous methodology for a low-cost commercial AR.Drone 2.0 in partly unknown indoor flight using only on-board visual and internal sensing. Novelty lies in: (i) the development of a position-estimation method using sensor fusion in a structured environment. This localization method presents how to get the UAV localization states (position and orientation), through a sensor fusion scheme, dealing with data provided by an optical sensor and an inertial measurement unit (IMU). Such a data fusion scheme takes also in to account the time delay present in the camera signal due to the communication protocols; (ii) improved potential field method which is capable of performing obstacle avoiding in an unknown environment and solving the non-reachable goal problem; and (iii) the design and implementation of an optimal proportional - integral - derivative (PID) controller based on a novel multi-objective particle swarm optimization with an accelerated update methodology tracking such reference trajectories, thus characterizing a cascade controller. Experimental results validate the effectiveness of the proposed approach.

Fuzzy Motion Planning for Nonholonomic Mobile Robot Navigation in Unknown Indoor Environments

Fuzzy Motion Planning for Nonholonomic Mobile Robot Navigation in Unknown Indoor Environments

Distributed Multi-agent Bidding-Based Approach for the Collaborative Mapping of Unknown Indoor Environments by a Homogeneous Mobile Robot Team

Abstract This paper deals with the problem of the collaborative mapping of unknown indoor environments by a homogeneous mobile robot team. For this aim, a distributed multi-agent coordination approach is proposed for the mapping process to offer a global view of the entire environment. First, the scheme starts by assigning the most suitable robots to the different zones of the environment to be mapped based on a bidding strategy. Then, while a Robot agent of the group explores its local surroundings and collects information about its neighborhood, it sends mapping data to the Human/Machine Interface agent to integrate them into a single global map. Furthermore, a geometric map representation and an algorithm based on obstacles and environment limits detection are used to provide an explicitly geometric representation of the workspace. For validation purposes, Player/Stage simulator is used to show the effectiveness of the proposed distributed approach and algorithms without needing a real multi-robot system and environment. Finally, various scenarios have been carried out and results are compared in terms of (i) required mapping time, (ii) accuracy of the global generated map, and (iii) number of exchanged messages between the agents.

Obstacle Avoidance Strategy using Onboard Stereo Vision on a Flapping Wing MAV

The development of autonomous lightweight MAVs, capable of navigating in unknown indoor environments, is one of the major challenges in robotics. The complexity of this challenge comes from constraints on weight and power consumption of onboard sensing and processing devices. In this paper we propose the "Droplet" strategy, an avoidance strategy based on stereo vision inputs that outperforms reactive avoidance strategies by allowing constant speed maneuvers while being computationally extremely efficient, and which does not need to store previous images or maps. The strategy deals with nonholonomic motion constraints of most fixed and flapping wing platforms, and with the limited field-of-view of stereo camera systems. It guarantees obstacle-free flight in the absence of sensor and motor noise. We first analyze the strategy in simulation, and then show its robustness in real-world conditions by implementing it on a 20-gram flapping wing MAV.

Exploration and Mapping of Unknown Polygonal Environments Based on Uncertain Range Data

We consider problem of exploration and mapping of unknown indoor environments using laser range finder. We assume a setup with a resolved localization problem and known uncertainty sensor models. Most exploration algorithms are based on detection of a boundary between explored and unexplored regions. They are, however, not efficient in practice due to uncertainties in measurement, localization and map building. The exploration and mapping algorithm is proposed that extends Ekman's exploration algorithm by removing rigid constraints on the range sensor and robot localization. The proposed algorithm includes line extraction algorithm developed by Pfister, which incorporates noise models of the range sensor and robot's pose uncertainty. A line representation of the range data is used for creating polygon that represents explored region from each measurement pose. The polygon edges that do not correspond to real environmental features are candidates for a new measurement pose. A general polygon clipping algorithm is used to obtain the total explored region as the union of polygons from different measurement poses. The proposed algorithm is tested and compared to the Ekman's algorithm by simulations and experimentally on a Pioneer 3DX mobile robot equipped with SICK LMS-200 laser range finder.

미지 동적 환경에서 다중 이동로봇의 GA-Fuzzy 기반 자율항법

The work present in this paper deals with a navigation problem for multiple mobile robots in unknown indoor environments. The environments are completely unknown to the robots; thus, proximity sensors installed on the robots’ bodies must be used to detect information about the surroundings. The environments simulated in this work are dynamic ones which contain not only static but also moving obstacles. In order to guide the robot to move along a collision-free path and reach the goal, this paper presented a navigation method based on fuzzy approach. Then genetic algorithms were applied to optimize the membership functions and rules of the fuzzy controller. The simulation results verified that the proposed method effectively addresses the mobile robot navigation problem.

Hotspot Ranking Based Indoor Mapping and Mobility Analysis Using Crowdsourced Wi-Fi Signal

In this paper, we propose a new hotspot ranking-based indoor mapping and mobility analysis approach based on the sporadically collected crowdsourced Wi-Fi received signal strength (RSS) data. This approach aims to construct the indoor mapping, as well as achieve the mobility analysis of the users following their daily motion patterns in target environment. First, we perform the wavelet analysis with respect to each RSS sequence to mitigate the noise interference to some extent. Second, we develop a new multidimensional scaling approach to map each RSS data into a linear one in the 2-D signal space, which is followed by the density clustering approach to merge the linear ones into different clusters based on the spatial correlation property. Finally, we construct the indoor mapping from the signal into physical spaces by the concept of hotspot ranking order, as well as the transfer relations among different RSS clusters and different physical sub-areas. The experimental results demonstrate that the proposed approach can achieve the superior performance in terms of indoor mapping and mobility analysis in an unknown indoor environment.

P.2.b.030 - Peripapillary retinal nerve fiber layer thickness in major depressive patients treated with repetitive TMS

Vision and AHRS (attitude and heading reference system) sensors fusion strategy is prevalent in recent years for the legged robot's SLAM (Simultaneous Localization and Mapping), due to its low cost and effectiveness in the global positioning system. In this paper, a new adaptive estimation algorithm is proposed to achieve the robot SLAM by fusing binocular vision and AHRS sensors. A novel acceleration algorithm for SIFT implementation based on Compute Unified Device Architecture (CUDA) is presented to detect the matching feature points in 2D images. All the steps of SIFT were specifically distributed and implemented by CPU or GPU, according to the step's characteristics to make full use of computational resources. The registration of the 3D feature point cloud is performed by using the iterative closest point (ICP) algorithm. Our GPU-based SIFT implementation can run at the speed of 30 frames per second (fps) on most images with 900 × 750 resolution in the test. Compared to other methods, our algorithm is simple to implement and suitable for parallel processing. It can be easily integrated into mobile robot’s tasks like navigation or object tracking, which need the real-time localization information. Experiments results showed that in the unknown indoor environments, the proposed algorithm`s operation is stable and the positioning accuracy is high.

Improving the safety and the operational efficiency of emergency operators via on field situational awareness

In rescue missions, the situational awareness represents an essential tool in supporting rescue team operating in unknown and complex indoor environments. In case of fire in highly congested industrial scenarios (e.g., refineries, oil depots, petrochemical plants, etc.), the smoke may reduce the awareness of the rescuer about potential local resources/hazards, affecting both operational efficiency and personal safety. The mitigation of potential consequences arising from major accidents can be limited providing the emergency staff with tools able to foster their role on field. In this paper, we present the RISING (indooR localizatIon and building maintenance using radio frequency Identification and inertial NaviGation) project that is devoted to support on field operators supplying them with a system for situational awareness and personal indoor positioning. The RISING solution is based on the integration of the RFID technology with the inertial navigation. A set of RFID tags, conveniently preinstalled in the working environment, can store information about their absolute position and the site of local items. This information can be easily retrieved on-the-fly using RFID readers and displayed on smart devices with which the user is equipped (e.g., tablet and/or smartphone) to allow on field situational awareness.

Co-Robotic Cane: A New Robotic Navigation Aid for the Visually Impaired.

This paper presents a new robotic navigation aid, called Co-Robotic Cane (CRC). The CRC uses a 3D camera for both pose estimation and object recognition in an unknown indoor environment. The 6-DOF pose estimation method determines the CRC's pose change by an egomotion estimation method and the iterative closest point algorithm and reduces the pose integration error by a pose graph optimization algorithm. The pose estimation method does not require any prior knowledge of the environment. The object recognition method detects indoor structures such as stairways, doorways, etc. and objects such as tables, computer monitors, etc. by a Gaussian Mixture Model based pattern recognition method. Some structures/objects (e.g., stairways) can be used as navigational waypoints and the others for obstacle avoidance. The CRC can be used in either robot cane (active) mode or white cane (passive) mode. In the active mode it guides the user by steering itself into the desired direction of travel, while in the passive mode it functions as a computer-vision-enhanced white cane. The CRC is a co-robot. It can detect human intent and use the intent to select a suitable mode automatically.