Unknown Indoor Environment Research Articles

Cellular Communication-Based Autonomous UAV Navigation with Obstacle Avoidance for Unknown Indoor Environments

Cellular Communication-Based Autonomous UAV Navigation with Obstacle Avoidance for Unknown Indoor Environments

Map Merging with Suppositional Box for Multi-Robot Indoor Mapping

For the map building of unknown indoor environment, compared with single robot, multi-robot collaborative mapping has higher efficiency. Map merging is one of the fundamental problems in multi-robot collaborative mapping. However, in the process of grid map merging, image processing methods such as feature matching, as a basic method, are challenged by low feature matching rate. Driven by this challenge, a novel map merging method based on suppositional box that is constructed by right-angled points and vertical lines is proposed. The paper firstly extracts right-angled points of suppositional box selected from the vertical point which is the intersection of the vertical line. Secondly, based on the common edge characteristics between the right-angled points, suppositional box in the map is constructed. Then the transformation matrix is obtained according to the matching pair of suppositional boxes. Finally, for matching errors based on the length of pairs, Kalman filter is used to optimize the transformation matrix. Experimental results show that this method can effectively merge map in different scenes and the successful matching rate is greater than that of other features.

An Outline of Multi-Sensor Fusion Methods for Mobile Agents Indoor Navigation

Indoor autonomous navigation refers to the perception and exploration abilities of mobile agents in unknown indoor environments with the help of various sensors. It is the basic and one of the most important functions of mobile agents. In spite of the high performance of the single-sensor navigation method, multi-sensor fusion methods still potentially improve the perception and navigation abilities of mobile agents. This work summarizes the multi-sensor fusion methods for mobile agents’ navigation by: (1) analyzing and comparing the advantages and disadvantages of a single sensor in the task of navigation; (2) introducing the mainstream technologies of multi-sensor fusion methods, including various combinations of sensors and several widely recognized multi-modal sensor datasets. Finally, we discuss the possible technique trends of multi-sensor fusion methods, especially its technique challenges in practical navigation environments.

Mutual Positioning Method in Unknown Indoor Environment Based on Visual Image Semantics

In our society, realizing intelligent positioning in indoor environments is important to build a smart city. Currently, mutual positioning requirements in the unknown indoor environment are growing fast. However, in such environment, we can obtain neither outdoor radio signal nor the indoor images in advance for online positioning. Therefore, how to achieve mutual positioning becomes an interesting problem. In this paper, we propose a vision‐based mutual positioning method in an unknown indoor environment. First, two users take images of the unknown indoor environment, use semantic segmentation network to identify the semantic targets contained in the images, and upload the generated semantic sequence to the user shared database in real time. Then, every time two users reupload a semantic sequence due to a change of location, it is necessary to retrieve whether another user has uploaded the same semantic sequence in the shared database. If the retrieval is successful, it means that two users have seen the same scene. Finally, two users select a target from the two user images taken based on the same scene to establish a three‐dimensional coordinate system, respectively, calculate their own position coordinates in this coordinate system, and realize mutual positioning through position coordinate sharing. Experiment results show that our proposed method can successfully realize mutual positioning between two users in an unknown indoor environment, while ensuring high positioning accuracy.

Integrating Wearable Haptics and Obstacle Avoidance for the Visually Impaired in Indoor Navigation: A User-Centered Approach.

Recently, in the attempt to increase blind people autonomy and improve their quality of life, a lot of effort has been devoted to develop technological travel aids. These systems can surrogate spatial information about the environment and deliver it to end-users through sensory substitution (auditory, haptic). However, despite the promising research outcomes, these solutions have met scarce acceptance in real-world. Often, this is also due to the limited involvement of real end users in the conceptual and design phases. In this article, we propose a novel indoor navigation system based on wearable haptic technologies. All the developmental phases were driven by continuous feedback from visually impaired persons. The proposed travel aid system consists of a RGB-D camera, a processing unit to compute visual information for obstacle avoidance, and a wearable device, which can provide normal and tangential force cues for guidance in an unknown indoor environment. Experiments with blindfolded subjects and visually impaired participants show that our system could be an effective support during indoor navigation, and a viable tool for training blind people to the usage of travel aids.

Memory-Based Deep Reinforcement Learning for Obstacle Avoidance in UAV With Limited Environment Knowledge

This paper presents our method for enabling a UAV quadrotor, equipped with a monocular camera, to autonomously avoid collisions with obstacles in unstructured and unknown indoor environments. When compared to obstacle avoidance in ground vehicular robots, UAV navigation brings in additional challenges because the UAV motion is no more constrained to a well-defined indoor ground or street environment. Unlike ground vehicular robots, a UAV has to navigate across more types of obstacles - for e.g., objects like decorative items, furnishings, ceiling fans, sign-boards, tree branches, etc., are also potential obstacles for a UAV. Thus, methods of obstacle avoidance developed for ground robots are clearly inadequate for UAV navigation. Current control methods using monocular images for UAV obstacle avoidance are heavily dependent on environment information. These controllers do not fully retain and utilize the extensively available information about the ambient environment for decision making. We propose a deep reinforcement learning based method for UAV obstacle avoidance (OA) which is capable of doing exactly the same. The crucial idea in our method is the concept of partial observability and how UAVs can retain relevant information about the environment structure to make better future navigation decisions. Our OA technique uses recurrent neural networks with temporal attention and provides better results compared to prior works in terms of distance covered without collisions. In addition, our technique has a high inference rate and reduces power wastage as it minimizes oscillatory motion of UAV.

Path Planning for Smart Car Based on Dijkstra Algorithm and Dynamic Window Approach

Path planning and obstacle avoidance are essential for autonomous driving cars. On the base of a self‐constructed smart obstacle avoidance car, which used a LeTMC‐520 depth camera and Jetson controller, this paper established a map of an unknown indoor environment based on depth information via SLAM technology. The Dijkstra algorithm is used as the global path planning algorithm and the dynamic window approach (DWA) as its local path planning algorithm, which are applied to the smart car, enabling it to successfully avoid obstacles from the planned initial position and reach the designated position. The tests on the smart car prove that the system can complete the functions of environment map establishment, path planning and navigation, and obstacle avoidance.

Research on Mobile Robot Positioning and Navigation System Based on Multi-sensor Fusion

The mobile robot integrates walking and operation functions, which can greatly expand the application range of traditional industrial robots. Aiming at the shortcomings of single sensor positioning under unknown indoor environment, such as large accumulated error and environmental limitations, a multi-sensor fusion positioning navigation system is designed to improve the positioning accuracy of autonomous navigation of mobile robots. The mobile robot uses the laser sensor as the core sensor to obtain two-dimensional information of the environment and realize real-time display of algorithms and interfaces such as map construction, positioning and navigation. The data results of the indoor navigation experiment show that the system has good robustness. At the same time, the feasibility and reliability of the system are verified.

Experimental Analysis of a Spatialised Audio Interface for People with Visual Impairments

Sound perception is a fundamental skill for many people with severe sight impairments. The research presented in this article is part of an ongoing project with the aim to create a mobile guidance aid to help people with vision impairments find objects within an unknown indoor environment. This system requires an effective non-visual interface and uses bone-conduction headphones to transmit audio instructions to the user. It has been implemented and tested with spatialised audio cues, which convey the direction of a predefined target in 3D space. We present an in-depth evaluation of the audio interface with several experiments that involve a large number of participants, both blindfolded and with actual visual impairments, and analyse the pros and cons of our design choices. In addition to producing results comparable to the state-of-the-art, we found that Fitts’s Law (a predictive model for human movement) provides a suitable metric that can be used to improve and refine the quality of the audio interface in future mobile navigation aids.

Peer-to-Peer Localization for Single-Antenna Devices

Some important indoor localization applications, such as localizing a lost kid in a shopping mall, call for a new peer-to-peer localization technique that can localize an individual's smartphone or wearables by directly using another's on-body devices in unknown indoor environments. However, current localization solutions either require pre-deployed infrastructures or multiple antennas in both transceivers, impending their wide-scale application. In this paper, we present P2PLocate, a peer-to-peer localization system that enables a single-antenna device co-located with a batteryless backscatter tag to localize another single-antenna device with decimeter-level accuracy. P2PLocate leverages the multipath variations intentionally created by an on-body backscatter tag, coupled with spatial information offered by user movements, to accomplish this objective without relying on any pre-deployed infrastructures or pre-training. P2PLocate incorporates novel algorithms to address two major challenges: (i) interference with strong direct-path signal while extracting multipath variations, and (ii) lack of direction information while using single-antenna transceivers. We implement P2PLocate on commercial off-the-shelf Google Nexus 6p, Intel 5300 WiFi card, and Raspberry Pi B4. Real-world experiments reveal that P2PLocate can localize both static and mobile targets with a median accuracy of 0.88 m.

Multivariable Event-Triggered Generalized Super-Twisting Controller for Safe Navigation of Nonholonomic Mobile Robot

This article presents a multivariable event-triggered generalized super-twisting sliding-mode algorithm for a nonholonomic mobile-robot safe navigation in unknown indoor environment. For robot safe navigation, vision-based variable gain composite guidance law is proposed to generate desired angular velocity. The guidance strategy uses the centroid of the depth map of the obstacles obtained from RGB-depth sensor. The propose controller follows the path generated by the guidance law. Stability analysis of the controller is done to show the fast convergence as compared to the existing controllers. To show the robustness of the controller in comparison to the existing super-twisting algorithm, a sensitivity analysis is carried out. Furthermore, for minimizing the controller effort, an event-triggered condition for the proposed controller is obtained using Lyapunov stability theory. Comparative simulation study is carried out to show the fast convergence and robustness of the proposed controller. Experiments are done for robot safe navigation in presence of disturbance.

Robotic Mapping Using Autonomous Vehicle

Mapping the interior of the building is the first step in order to obtain the description of that building. In this work, an autonomous robot is designed and built. The robot is able to navigate through an unknown environment, avoiding obstacles and simultaneously build a 3D map on its own. This autonomous feature is implemented using minimal and economically viable hardware such as Kinect v2 sensor and Arduino. Also, an open-source platform Robotic Operating System is used, on top of which the mapping and navigation algorithms are implemented. The 3D map built can be used with a lot of existing devices which involves localization. Post-processing procedures are used to obtain point clouds for building the 3D map. Emphasis is put on the efficient use of the available resources to generate the 3D maps of unknown indoor environment.

Exploration for Object Mapping Guided by Environmental Semantics using UAVs

This paper presents a strategy to autonomously explore unknown indoor environments, focusing on 3D mapping of the environment and performing grid level semantic labeling to identify all available objects. Unlike conventional exploration techniques that utilize geometric heuristics and information gain theory on an occupancy grid map, the work presented in this paper considers semantic information, such as the class of objects, in order to gear the exploration towards environmental segmentation and object labeling. The proposed approach utilizes deep learning to map 2D semantically segmented images into 3D semantic point clouds that encapsulate both occupancy and semantic annotations. A next-best-view exploration algorithm is employed to iteratively explore and label all the objects in the environment using a novel utility function that balances exploration and semantic object labeling. The proposed strategy was evaluated in a realistically simulated indoor environment, and results were benchmarked against other exploration strategies.

Path Planning for Mobile Robot Navigation in Unknown Indoor Environments Using Hybrid PSOFS Algorithm

Over the past few years, mobile robots are widely used in various industries because they can navigate in dynamic environments and carry out everyday tasks efficiently. Path planning undoubtedly plays an important role in mobile robot navigation, thus becoming one of the most researched topics in the field of robotics. The metaheuristic algorithms have been extensively studied in recent years to solve the path planning problems the same way as the optimization problems were solved, or in other words, plan the optimum path for the robot to navigate from the starting point to the destination. In this research, a mobile robot is launched to find its path from the starting point to the destination in three different simulated environments using a proposed hybrid metaheuristic algorithm between Particle Swarm Optimization and Fringe Search Algorithm, named PSOFS, and its simultaneous localization and mapping (SLAM) capability. During runtime, the path is optimized by considering the path length. The performance of PSOFS for local path planning is compared against two existing algorithms by evaluating the path smoothness as well as robot safety. The results showed that PSOFS has successfully generated shorter, smoother, and safer paths than the algorithms for mobile robot navigation in unknown indoor environments.

Perspective Independent Ground Plane Estimation by 2D and 3D Data Analysis

Identifying the orientation and location of a camera placed arbitrarily in a room is a challenging problem. Existing approaches impose common assumptions (e.g. the ground plane is the largest plane in the scene, the camera roll angle is zero). We present a method for estimating the ground plane and camera orientation in an unknown indoor environment given RGB-D data (colour and depth) from a camera with arbitrary orientation and location assuming that at least one person can be seem smoothly moving within the camera field of view with their body perpendicular to the ground plane. From a set of RGB-D data trials captured using a Kinect sensor, we develop an approach to identify potential ground planes, cluster objects in the scenes and find 2D Scale-Invariant Feature Transform (SIFT) keypoints for those objects, and then build a motion sequence for each object by evaluating the intersection of each object's histogram in three dimensions across frames. After finding the reliable homography for all objects, we identify the moving human object by checking the change in the histogram intersection, object dimensions and the trajectory vector of the homgraphy decomposition. We then estimate the ground plane from the potential planes using the normal vector of the homography decomposition, the trajectory vector, and the spatial relationship of the planes to the other objects in the scene. Our results show that the ground plane can be successfully detected, if visible, regardless of camera orientation, ground plane size, and movement speed of the human. We evaluated our approach on our own data and on three public datasets, robustly estimating the ground plane in all indoor scenarios. Our successful approach substantially reduces restrictions on a prior knowledge of the ground plane, and has broad application in conditions where environments are dynamic and cluttered, as well as fields such as automated robotics, localization and mapping.

Collaborative SLAM Based on WiFi Fingerprint Similarity and Motion Information

Simultaneous localization and mapping (SLAM) has been extensively researched in past years particularly with regard to range-based or visual-based sensors. Instead of deploying dedicated devices that use visual features, it is more pragmatic to exploit the radio features to achieve this task, due to their ubiquitous nature and the widespread deployment of the Wi-Fi wireless network. This article presents a novel approach for collaborative simultaneous localization and radio fingerprint mapping (C-SLAM-RF) in large unknown indoor environments. The proposed system uses received signal strengths (RSS) from Wi-Fi access points (APs) in the existing infrastructure and pedestrian dead reckoning (PDR) from a smartphone, without a prior knowledge about map or distribution of AP in the environment. We claim a loop closure based on the similarity of the two radio fingerprints. To further improve the performance, we incorporate the turning motion and assign a small uncertainty value to a loop closure if a matched turning is identified. The experiment was done in an area of 130 m by 70 m and the results show that our proposed system is capable of estimating the tracks of four users with an accuracy of 0.6 m with Tango-based PDR and 4.76 m with a step counter-based PDR.

Indoor Mapping Guidance Algorithm of Rotary-Wing UAV Including Dead-End Situations.

A mapping guidance algorithm of a quadrotor for unknown indoor environments is proposed. A sensor with limited sensing range is assumed to be mounted on the quadrotor to obtain object data points. With obtained data, the quadrotor computes velocity vector and yaw commands to move around the object while maintaining a safe distance. The magnitude of the velocity vector is also controlled to prevent a collision. The distance transform method is applied to establish dead-end situation logic as well as exploration completion logic. When a dead-end situation occurs, the guidance algorithm of the quadrotor is switched to a particular maneuver. The proposed maneuver enables the quadrotor not only to escape from the dead-end situation, but also to find undiscovered area to continue mapping. Various numerical simulations are performed to verify the performance of the proposed mapping guidance algorithm.

SLAM-GDM Implementation on Robot Operating System for Gas Source Localization

Gas Distribution Mapping (GDM) can be defined as visualization for the spatial distribution of gas concentration in an unknown indoor environment. Recently, incidents of gas leakage have increased gradually worldwide. This will significantly increase the casualty rate at the workplace. This paper focusses on the implementation of Kernel DM based SLAM- GDM on Robot Operating System (ROS), a novel approach that will be useful in the effort of localizing the gas source during gas leakage inspection. This approach has been conducted successfully by using ROS-architecture implemented system, tested in simulation and on a mobile robot. The result shows that this implementation achieved average accuracy of 0.71m and 0.32m and therefore deemed suitable for gas source location prediction.

Autonomous 3-D Reconstruction, Mapping, and Exploration of Indoor Environments With a Robotic Arm

We propose a novel information gain metric that combines hand-crafted and data-driven metrics to address the next best view problem for autonomous 3-D mapping of unknown indoor environments. For the hand-crafted metric, we propose an entropy-based information gain that accounts for the previous view points to avoid the camera to revisit the same location and to promote the motion toward unexplored or occluded areas. However, for the learnt metric, we adopt a convolutional neural network (CNN) architecture and formulate the problem as a classification problem. The CNN takes the current depth image as input and outputs the motion direction that suggests the largest unexplored surface. We train and test the CNN using a new synthetic dataset based on the SUNCG dataset. The learnt motion direction is then combined with the proposed hand-crafted metric to help handle situations where using only the hand-crafted metric tends to face ambiguities. We finally evaluate the autonomous paths over several real and synthetic indoor scenes including complex industrial and domestic settings and prove that our combined metric is able to further improve the exploration coverage compared to using only the proposed hand-crafted metric.

An Accurate Visual-Inertial Integrated Geo-Tagging Method for Crowdsourcing-Based Indoor Localization

One of the unavoidable bottlenecks in the public application of passive signal (e.g., received signal strength, magnetic) fingerprinting-based indoor localization technologies is the extensive human effort that is required to construct and update database for indoor positioning. In this paper, we propose an accurate visual-inertial integrated geo-tagging method that can be used to collect fingerprints and construct the radio map by exploiting the crowdsourced trajectory of smartphone users. By integrating multisource information from the smartphone sensors (e.g., camera, accelerometer, and gyroscope), this system can accurately reconstruct the geometry of trajectories. An algorithm is proposed to estimate the spatial location of trajectories in the reference coordinate system and construct the radio map and geo-tagged image database for indoor positioning. With the help of several initial reference points, this algorithm can be implemented in an unknown indoor environment without any prior knowledge of the floorplan or the initial location of crowdsourced trajectories. The experimental results show that the average calibration error of the fingerprints is 0.67 m. A weighted k-nearest neighbor method (without any optimization) and the image matching method are used to evaluate the performance of constructed multisource database. The average localization error of received signal strength (RSS) based indoor positioning and image based positioning are 3.2 m and 1.2 m, respectively, showing that the quality of the constructed indoor radio map is at the same level as those that were constructed by site surveying. Compared with the traditional site survey based positioning cost, this system can greatly reduce the human labor cost, with the least external information.