Unknown Indoor Environment Research Articles

A novel autonomous exploration algorithm via LiDAR/IMU SLAM and hierarchical subsystem for mobile robot in unknown indoor environments

Abstract Autonomous exploration in unknown environments is an essential capability for mobile robots. The complexity of autonomous exploration, however, means that existing algorithms struggle to balance efficiency and comprehensiveness, causing low mapping accuracy and redundant path planning. To perform accurate and efficient exploration tasks, we have proposed a novel autonomous exploration algorithm via LiDAR/IMU (Inertial Measurement Unit) Simultaneous Localization and Mapping (SLAM) and hierarchical subsystem for mobile robot in unknown environments. Firstly, to enhance mapping accuracy for mobile robot exploration, LiDAR/IMU SLAM is improved with the assistance of backward propagation and iterated Kalman filter, and Bidirectional Rapidly–Exploring Random Trees* (BI–RRT*) is applied for efficient frontier point detection. Secondly, we optimize local path planning by leveraging information theory through perceptual quality evaluation, which is then integrated with global path planning utilizing an enhanced Travelling Salesman Problem solver and a sparse grid map to amplify exploration efficiency. Thirdly, an enhanced hierarchical autonomous exploration method for mobile robots is proposed, which incorporates local path planning for seamless navigation around highly promising exploration spots, coupled with global path planning to effectively interconnect various sub–regions. Finally, simulations and field tests have demonstrated that the proposed method explores an unknown indoor environment with a 30.8% reduction in exploration time and a 29.9% reduction in exploration path in comparison with Dynamic Stage Viewpoint Planner. The map constructed in this paper has more accurate details and exploration paths have been shortened to ensure effective exploration.

Implementation of extended kalman filter for localization of ambulance robot

AbstractThis paper focuses on the implementation of the Extended Kalman Filter for indoor localization of a semi-autonomous Ambulance Robot system named Ambubot. The system is designed to reduce the response time for lay rescuers to locate an Automated External Defibrillator (AED) during sudden cardiac arrest events. To achieve this objective, the robot is equipped with an AED, and the Extended Kalman Filter is utilized for optimal indoor localization. The filter is implemented using data from the robot’s Inertial Measurement Unit, which comprises 9 Degrees of Freedom. The paper provides an explicit description of the performance of the Extended Kalman Filter in estimating the position of Ambubot, and demonstrates that the proposed approach is effective in accurately determining and estimating the robot’s position in unknown indoor environments. The results suggest that the proposed method is a promising solution for improving survival rates in cardiac arrest cases, and may have potential applications in other fields where accurate indoor localization is required.

Exploration- and Exploitation-Driven Deep Deterministic Policy Gradient for Active SLAM in Unknown Indoor Environments

This study proposes a solution for Active Simultaneous Localization and Mapping (Active SLAM) of robots in unknown indoor environments using a combination of Deep Deterministic Policy Gradient (DDPG) path planning and the Cartographer algorithm. To enhance the convergence speed of the DDPG network and minimize collisions with obstacles, we devised a unique reward function that integrates exploration and exploitation strategies. The exploration strategy allows the robot to achieve the shortest running time and movement trajectory, enabling efficient traversal of unmapped environments. Moreover, the exploitation strategy introduces active closed loops to enhance map accuracy. We conducted experiments using the simulation platform Gazebo to validate our proposed model. The experimental results demonstrate that our model surpasses other Active SLAM methods in exploring and mapping unknown environments, achieving significant grid completeness of 98.7%.

Hybrid Vulture-Coordinated Multi-Robot Exploration: A Novel Algorithm for Optimization of Multi-Robot Exploration

Exploring unknown environments using multiple robots has numerous applications in various fields but remains a challenging task. This study proposes a novel hybrid optimization method called Hybrid Vulture-Coordinated Multi-Robot Exploration (HVCME), which combines Coordinated Multi-Robot Exploration (CME) and African Vultures Optimization Algorithm (AVOA) to optimize the construction of a finite map in multi-robot exploration. We compared HVCME with four other similar algorithms using three performance measures: run time, percentage of the explored area, and the number of times the method failed to complete a run. The experimental results show that HVCME outperforms the other four methods, demonstrating its effectiveness in optimizing the construction of a finite map in an unknown indoor environment.

Hardware-Efficient Scheme for Trailer Robot Parking by Truck Robot in an Indoor Environment with Rendezvous.

Autonomous grounded vehicle-based social assistance/service robot parking in an indoor environment is an exciting challenge in urban cities. There are few efficient methods for parking multi-robot/agent teams in an unknown indoor environment. The primary objective of autonomous multi-robot/agent teams is to establish synchronization between them and to stay in behavioral control when static and when in motion. In this regard, the proposed hardware-efficient algorithm addresses the parking of a trailer (follower) robot in indoor environments by a truck (leader) robot with a rendezvous approach. In the process of parking, initial rendezvous behavioral control between the truck and trailer robots is established. Next, the parking space in the environment is estimated by the truck robot, and the trailer robot parks under the supervision of the truck robot. The proposed behavioral control mechanisms were executed between heterogenous-type computational-based robots. Optimized sensors were used for traversing and the execution of the parking methods. The truck robot leads, and the trailer robot mimics the actions in the execution of path planning and parking. The truck robot was integrated with FPGA (Xilinx Zynq XC7Z020-CLG484-1), and the trailer was integrated with Arduino UNO computing devices; this heterogenous modeling is adequate in the execution of trailer parking by a truck. The hardware schemes were developed using Verilog HDL for the FPGA (truck)-based robot and Python for the Arduino (trailer)-based robot.

Monocular Based Navigation System for Autonomous Ground Robots Using Multiple Deep Learning Models

In recent years, the development of ground robots with human-like perception capabilities has led to the use of multiple sensors, including cameras, lidars, and radars, along with deep learning techniques for detecting and recognizing objects and estimating distances. This paper proposes a computer vision-based navigation system that integrates object detection, segmentation, and monocular depth estimation using deep neural networks to identify predefined target objects and navigate towards them with a single monocular camera as a sensor. Our experiments include different sensitivity analyses to evaluate the impact of monocular cues on distance estimation. We show that this system can provide a ground robot with the perception capabilities needed for autonomous navigation in unknown indoor environments without the need for prior mapping or external positioning systems. This technique provides an efficient and cost-effective means of navigation, overcoming the limitations of other navigation techniques such as GPS-based and SLAM-based navigation.Graphical

An Indoor Autonomous Inspection and Firefighting Robot Based on SLAM and Flame Image Recognition

Indoor fire accidents have become increasingly common in recent years. More and more firefighting robots have been designed to solve the problem of fires. However, the indoor environment is very complex, with high temperatures, thick smoke, more turns, and various burning substances. In this study, a firefighting robot with autonomous inspection and automatic fire-extinguishing functions intended for use in unknown indoor environments was designed. Considering water’s poor efficiency and its inability to extinguish some combustion materials, other fire extinguishers were applied to design the extinguishing system. The robot can install four different extinguishers as required and select the appropriate fire extinguisher to spray it automatically. Based on the Cartographer SLAM (simultaneous localization and mapping) theory, a robot map-building system was built using Lidar scanners, IMU (inertial measurement unit) sensors, encoders, and other sensors. The accurate identification and location of the fire source were achieved using an infrared thermal imager and the YOLOv4 deep learning algorithm. Finally, the performance of the firefighting robot was evaluated by creating a simulated-fire experimental environment. In an autonomous inspection process of the on-fire environment, the firefighting robot could identify the flame in real-time, trigger the fire-extinguishing system to carry out automatic fire extinguishing, and contain the fire in its embryonic stage.

Multi-Sensor Fusion-Based Indoor Single-Track Semantic Map Construction and Localization

The advancement of smartphones has been a prerequisite for multi-sensor fusion-based indoor mapping due to their high penetration and low cost. This article constructs a sparse single-track semantic map by fusing waypoints, semantic landmarks, and Wi-Fi landmarks, which are scene-representable and navigational in unknown indoor environments. The pedestrian dead reckoning (PDR)-aided visual-inertial simultaneous localization and mapping (PDR-aided VI-SLAM) method uses the PDR velocity as an external observation to constrain the pre-integration of inertial measurements. The semantic objects detected by the YOLO V4 are pre-filtered by the proposed semantic object filtering algorithm before semantics matching. The centroid of feature points clustered by the random-sampling spatial clustering algorithm (R-DBSCAN) characterizes the location of a semantic landmark relative to the waypoints estimated by the PDR-aided VI-SLAM. To enhance the stability of Wi-Fi landmarks, Wi-Fi signals are processed by the proposed sliding window-based Wi-Fi fusion algorithm. By using waypoints as an intermediate quantity, the association relationships between waypoints, semantic landmarks, and Wi-Fi landmarks are established. After that, a lightweight single-track semantic map is constructed. A landmark matching-based localization method is proposed to evaluate the similarity between the local map and the constructed venue map to infer the location of a pedestrian in the venue map. Experiments are conducted in the office building and mall scenes under various illumination conditions and semantic density. Results demonstrate the high quality of the single-track semantic map and high-precision localization of a local map in the constructed venue map.

Robot odor source localization in indoor environments based on gradient adaptive extremum seeking search

When toxic or harmful chemicals or pollutants leak, it is of great significance to determine the leakage source(s) in a timely and autonomous manner to reduce casualties and property losses. This study focuses on the problem of robot-based odor source localization (OSL) in unknown indoor environments from a control perspective. A model-free gradient adaptive extremum seeking control (GA-ESC) algorithm was proposed to improve the searching efficiency and success rate of OSL. The GA-ESC algorithm plans the OSL through a controller, which estimates the gradient of the odor plume concentration, and guides the robot to approach the odor source according to the estimated gradient. A novel three-point gradient estimation method was proposed to obtain a more stable odor field gradient based on two historical points and one current point, where an adaptive feedback gain was applied to link the estimated gradient and the output control quantities. Subsequently, the perturbation amplitude adjustment (PAA) strategy was introduced to enhance the global searching ability. When compared to the traditional extremum seeking control (ESC), our algorithm outperforms it in terms of success rate and average searching time. Moreover, the performance of the proposed algorithm was validated through simulations by utilizing three different dispersion models. The real-robot experiments were also carried out in indoor environments. The results demonstrate the significance of the proposed control-based OSL algorithm in unknown indoor environments.

Research on Autonomous Navigation of Showroom Service Robot Based on ROS and Laser SLAM

As an important tool for guiding and introducing visitors to the exhibition hall, the robot needs to confront the complex environment that may exist and the constantly moving visitors. First of all, it is needed to have the ability to enable accurate positioning and navigation as well as dynamic obstacle avoidance. In recent years, simultaneous localization and mapping (SLAM) technology has been widely used to address localization and navigation issues in unknown indoor environments. Based on the robot operating system (ROS), this study collects environmental information by 2D LiDAR, and then combines the service robot's Inertial measurement unit (IMU), odometer, and other sensors to map the exhibition hall environment by the Cartographer SLAM algorithm. Path planning and dynamic obstacle avoidance functions are also implemented, in path planning, the algorithm is selected for global path planning and the Dynamic Window Approach (DWA) algorithm for local path planning. The environment map construction, path planning and dynamic obstacle avoidance functions are verified by simulation and in real environment. The average position deviation and standard deviation (SD) of the robot from the target point were less than 7 cm and less than 3 cm, respectively, when the robot was set to move at 0.5 m/s for the positioning test. The average heading deviation was less than 9° and SD was less than 3°. The robot's positioning and navigation can well meet the working requirements of the pavilion service robot.

Mobile Robot Navigation Using Deep Reinforcement Learning

Learning how to navigate autonomously in an unknown indoor environment without colliding with static and dynamic obstacles is important for mobile robots. The conventional mobile robot navigation system does not have the ability to learn autonomously. Unlike conventional approaches, this paper proposes an end-to-end approach that uses deep reinforcement learning for autonomous mobile robot navigation in an unknown environment. Two types of deep Q-learning agents, such as deep Q-network and double deep Q-network agents are proposed to enable the mobile robot to autonomously learn about collision avoidance and navigation capabilities in an unknown environment. For autonomous mobile robot navigation in an unknown environment, the process of detecting the target object is first carried out using a deep neural network model, and then the process of navigation to the target object is followed using the deep Q-network or double deep Q-network algorithm. The simulation results show that the mobile robot can autonomously navigate, recognize, and reach the target object location in an unknown environment without colliding with static and dynamic obstacles. Similar results are obtained in real-world experiments, but only with static obstacles. The DDQN agent outperforms the DQN agent in reaching the target object location in the test simulation by 5.06%.

Asynchronous Collaborative Autoscanning with Mode Switching for Multi-Robot Scene Reconstruction

When conducting autonomous scanning for the online reconstruction of unknown indoor environments, robots have to be competent at exploring scene structure and reconstructing objects with high quality. Our key observation is that different tasks demand specialized scanning properties of robots: rapid moving speed and far vision for global exploration and slow moving speed and narrow vision for local object reconstruction, which are referred as two different scanning modes: explorer and reconstructor , respectively. When requiring multiple robots to collaborate for efficient exploration and fine-grained reconstruction, the questions on when to generate and how to assign those tasks should be carefully answered. Therefore, we propose a novel asynchronous collaborative autoscanning method with mode switching, which generates two kinds of scanning tasks with associated scanning modes, i.e., exploration task with explorer mode and reconstruction task with reconstructor mode, and assign them to the robots to execute in an asynchronous collaborative manner to highly boost the scanning efficiency and reconstruction quality. The task assignment is optimized by solving a modified Multi-Depot Multiple Traveling Salesman Problem (MDMTSP). Moreover, to further enhance the collaboration and increase the efficiency, we propose a task-flow model that actives the task generation and assignment process immediately when any of the robots finish all its tasks with no need to wait for all other robots to complete the tasks assigned in the previous iteration. Extensive experiments have been conducted to show the importance of each key component of our method and the superiority over previous methods in scanning efficiency and reconstruction quality.

Distributed Collaborative Pedestrian Inertial SLAM With Unknown Initial Relative Poses

Collaborative indoor positioning techniques for pedestrians have been extensively researched in the past years, particularly concerning the range-based collaborative indoor positioning system. However, range-based indoor collaboration methods suffer from nonline-of-sight (NLOS) and electromagnetic signal loss in indoor environments. Meanwhile, these methods require prior knowledge of the initial relative pose of the pedestrian, which is difficult to obtain in such an environment. To overcome the unreliable mutual observation and the lack of initial relative poses information in collaborative position systems, this article proposes a distributed collaborative inertial simultaneous localization and mapping (DCOGI-SLAM) framework for collaborative pedestrian positioning systems in unknown indoor environments without prior information. A coarse alignment method of relative poses based on encounter events is proposed to obtain the initial relative poses of pedestrians. A mutual position observation is constructed based on the map constructed through the occupancy grid-based inertial SLAM (OGI-SLAM) method to provide a stable innovation for collaborative correction of positioning errors, insulating the system from the NLOS and the loss of ranging signals. Moreover, a distributed collaborative occupancy grid-based inertial simultaneous localization and mapping (DCOGI-SLAM) framework is proposed to enable each pedestrian positioning system in a formation to operate independently. In a two-person collaborative experiment over a space of approximately 2500 m2, the proposed system can obtain comparable accuracy to single OGI-SLAM with known initial relative position information, when the initial relative position information is unknown. The average positioning error of the proposed method is 1.48 m.

Camera view planning based on generative adversarial imitation learning in indoor active exploration

Actively exploring unknown indoor environments using an RGB-D camera is a challenging task, especially when utilizing the feature-based visual simultaneous localization and mapping (vSLAM) methods. The existence of low-texture scenes, such as narrow corridors and white walls, may lead to frequent tracking failure of indoor features. To avoid tracking failure, while simultaneously ensuring adequate exploration, a novel active vSLAM framework with camera view planning based on generative adversarial imitation learning (GAIL) is proposed to actively adjust the orientation of the camera during robot motion. First, the Oriented FAST and Rotated BRIEF-SLAM2 (ORB-SLAM2) method is modified to reconstruct a three-channel navigation map that contains information about obstacles and explored areas. Second, a large number of view planning behaviors of human beings are collected in different indoor environments as the expert demonstration. Last, to make the robot imitate the human searching behaviors, the structures of the actor, critic, and discriminator networks are designed, and the GAIL method is used to train the camera view planning policy. Simulation results on a public dataset of indoor environments show that the proposed GAIL-SLAM framework improves the exploration coverage ratio of unknown environments by an average of 53.08% (34.21% for traditional vs. 87.29% for our proposed). Meanwhile, the number of effective exploration steps before the occurrence of tracking failure increases by 405% (73 for traditional vs. 369 for our proposed) on average, indicating that the rate of tracking failure is effectively reduced.

Deep-Reinforcement-Learning-Based Autonomous Establishment of Local Positioning Systems in Unknown Indoor Environments

Local positioning systems (LPSs) serve as a feasible alternative to provide positioning service in global navigation satellite system (GNSS)-denied environments. When the area of interest is unknown and potentially dangerous, e.g., urban search and rescue (USAR), or unreachable, e.g., extraterrestrial exploration, the autonomous establishment of LPSs by a robot is an attractive approach to coping with the demand for positioning service. In this article, we investigate the autonomous establishment problem in indoor scenarios, where a robot carrying several positioning beacons intends to place them sequentially to establish high-quality positioning services for the area of interest. To solve the complicated sequential decision problem, we first model the optimal positioning beacon configuration problem and then model the autonomous establishment process as a partially observable Markov decision process (POMDP). We apply deep reinforcement learning (DRL) to solve the POMDP. Extensive simulations, including comparisons with other baselines and generalization experiments, demonstrate the advantages of the proposed DRL-based autonomous establishment of LPSs.

Autonomous Exploration of Unknown Indoor Environments for High-Quality Mapping Using Feature-Based RGB-D SLAM

Simultaneous localization and mapping (SLAM) system-based indoor mapping using autonomous mobile robots in unknown environments is crucial for many applications, such as rescue scenarios, utility tunnel monitoring, and indoor 3D modeling. Researchers have proposed various strategies to obtain full coverage while minimizing exploration time; however, mapping quality factors have not been considered. In fact, mapping quality plays a pivotal role in 3D modeling, especially when using low-cost sensors in challenging indoor scenarios. This study proposes a novel exploration algorithm to simultaneously optimize exploration time and mapping quality using a low-cost RGB-D camera. Feature-based RGB-D SLAM is utilized due to its various advantages, such as low computational cost and dense real-time reconstruction ability. Subsequently, our novel exploration strategies consider the mapping quality factors of the RGB-D SLAM system. Exploration time optimization factors are also considered to set a new optimum goal. Furthermore, a Voronoi path planner is adopted for reliable, maximal obstacle clearance and fixed paths. According to the texture level, three exploration strategies are evaluated in three real-world environments. We achieve a significant enhancement in mapping quality and exploration time using our proposed exploration strategies compared to the baseline frontier-based exploration, particularly in a low-texture environment.

Autonomous Navigation System of Greenhouse Mobile Robot Based on 3D Lidar and 2D Lidar SLAM.

The application of mobile robots is an important link in the development of intelligent greenhouses. In view of the complex environment of a greenhouse, achieving precise positioning and navigation by robots has become the primary problem to be solved. Simultaneous localization and mapping (SLAM) technology is a hot spot in solving the positioning and navigation in an unknown indoor environment in recent years. Among them, the SLAM based on a two-dimensional (2D) Lidar can only collect the environmental information at the level of Lidar, while the SLAM based on a 3D Lidar demands a high computation cost; hence, it has higher requirements for the industrial computers. In this study, the robot navigation control system initially filtered the information of a 3D greenhouse environment collected by a 3D Lidar and fused the information into 2D information, and then, based on the robot odometers and inertial measurement unit information, the system has achieved a timely positioning and construction of the greenhouse environment by a robot using a 2D Lidar SLAM algorithm in Cartographer. This method not only ensures the accuracy of a greenhouse environmental map but also reduces the performance requirements on the industrial computer. In terms of path planning, the Dijkstra algorithm was used to plan the global navigation path of the robot while the Dynamic Window Approach (DWA) algorithm was used to plan the local navigation path of the robot. Through the positioning test, the average position deviation of the robot from the target positioning point is less than 8 cm with a standard deviation (SD) of less than 3 cm; the average course deviation is less than 3° with an SD of less than 1° at the moving speed of 0.4 m/s. The robot moves at the speed of 0.2, 0.4, and 0.6 m/s, respectively; the average lateral deviation between the actual movement path and the target movement path is less than 10 cm, and the SD is less than 6 cm; the average course deviation is <3°, and the SD is <1.5°. Both the positioning accuracy and the navigation accuracy of the robot can meet the requirements of mobile navigation and positioning in the greenhouse environment.

Deep Reinforcement Learning for Autonomous Map-Less Navigation of a Flying Robot

Flying robots are expected to be used in many tasks, such as aerial delivery, inspection inside dangerous areas, and rescue. However, their deployment in unstructured and highly dynamic environments has been limited. This paper proposes a novel approach for enabling a micro-aerial vehicle (MAV) system equipped with a laser rangefinder and depth sensor to autonomously navigate and explore an unknown indoor or outdoor environment. We built a modular deep-Q-network architecture to fuse information from multiple sensors mounted onboard a vehicle. The developed algorithm can perform collision-free flights in the real world, while being trained entirely on a 3D simulator. The proposed method does not require prior expert demonstrations, 3D mapping, or path planning. It transforms fused sensory data into a velocity control input for a robot through an end-to-end convolutional neural network (CNN). The obtained policy was compared to a simulation using the conventional potential-field method. Our approach achieves zero- shot transfer from simulation to real-world environments that were never experienced during training by simulating realistic sensor data. Several intensive experiments were conducted to demonstrate the effectiveness of our system for safely flying in dynamic outdoor and indoor environments. The supplementary videos for the actual flight tests can be accessed at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://bit.ly/2SEw8dQ</uri> .

A new PHD-SLAM method based on memory attenuation filter

Aiming at the problem that the low signal-to-noise ratio in the complex indoor environment will lead to the complex data association and low accuracy of the simultaneous localization and mapping (SLAM) method, a PHD-SLAM method based on the memory attenuation (MA) filter and mixed newborn maps information (MBMA-PHD-SLAM) is proposed in this paper. First of all, this method based on a probability hypothesis density (PHD) filter. Therefore, this method avoids data association and solves the problem of high computational complexity. Besides, the general PHD-SLAM method tends to cause the filter to diverge when the indoor signal-to-noise ratio is low. Therefore, MA filter is combined into PHD-SLAM. This method can reduce the influence of old data on SLAM, which improves the filtering divergence problem and improves the accuracy of SLAM. What’s more, since conventional SLAM algorithms usually have a problem of lack of prior information and in order to further improve the accuracy of SLAM on the basis of the above method, a method of mixed newborn maps information is proposed to solve this problem. Finally, the experiment mainly compares the method in this paper with the classic Rao-Blackwellised (RB) implementation of the PHD-SLAM (RB-PHD-SLAM). The results show that this method outperforms the PHD-SLAM method in terms of estimating map features and localization accuracy. The method in this paper effectively improves the ability of the mobile robot to explore unknown indoor environments.

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.