applsci-logo

Journal Browser

Journal Browser

Mobile Robots Navigation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (31 May 2019) | Viewed by 67035

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors


E-Mail Website
Guest Editor

E-Mail Website
Guest Editor
System Engineering and Automation Department, Miguel Hernandez University, 03202 Elche, Spain
Interests: computer vision; omnidirectional imaging; appearance descriptors; image processing; mobile robotics; environment modeling; visual localization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Navigation is one of the fundamental abilities that mobile robots must be endowed with, so that they can carry out high-level tasks autonomously, in a specific environment. This problem can be addressed efficiently through the following actions. First, it is necessary to perceive the environment in which the robot has to move, and extract some relevant information from it. Second, the robot must be able to solve the localization problem within this environment. With this information, a trajectory towards the target points must be planned, and the vehicle has to be guided along this trajectory, in a reactive way, considering either possible changes or interactions with the environment or with the user.

To perceive the environment, some kinds of onboard sensors can be used, such as laser rangefinders, visual systems, or RGB-D platforms. This perception task can be carried out either beforehand or once the navigation task has started, while the robot moves through the environment, and the result is a model or map of the environment. About the localization task, it must be designed considering several issues: the available sensors, the structure of the map, and the movement constraints that the robot presents (i.e. trajectories in 3D or 6D). Furthermore, integrated exploration systems consider all these issues jointly, and they develop trajectory planning and control, while a model of the environment is obtained, and the robot estimates its position and orientation within it.

The aim of this Special Issue is to present current frameworks in these fields and, in general, approaches to any problem related to the navigation of mobile robots. In this way, this Special Issue invites contributions to the following topics (but it is not limited to them):

  • Map-based navigation
  • Landmark-based navigation
  • Algorithms and methods for navigation
  • Data fusion for mobile robot navigation
  • Deep learning in mobile robot navigation
  • Vision-based mobile robot navigation
  • Motion control
  • Localization and environment modelling
  • Applications of mobile robot navigation

Prof. Dr. Oscar Reinoso
Prof. Dr. Luis Paya
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • exploration
  • path planning
  • visual mapping
  • mapping
  • localization
  • SLAM
  • navigation

Published Papers (15 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research

5 pages, 174 KiB  
Editorial
Special Issue on Mobile Robots Navigation
by Oscar Reinoso and Luis Payá
Appl. Sci. 2020, 10(4), 1317; https://doi.org/10.3390/app10041317 - 15 Feb 2020
Cited by 11 | Viewed by 2266
Abstract
In recent years, the presence of mobile robots in diverse scenarios has considerably increased, to solve a variety of tasks [...] Full article
(This article belongs to the Special Issue Mobile Robots Navigation)

Research

Jump to: Editorial

16 pages, 5681 KiB  
Article
A Method of Path Planning on Safe Depth for Unmanned Surface Vehicles Based on Hydrodynamic Analysis
by Shuai Liu, Chenxu Wang and Anmin Zhang
Appl. Sci. 2019, 9(16), 3228; https://doi.org/10.3390/app9163228 - 7 Aug 2019
Cited by 13 | Viewed by 2809
Abstract
The depth of water is of great significance to the safe navigation of unmanned surface vehicles (USV)in shallow waters, such as islands and reefs. How to consider the influence of depth on the safety of USV navigation and path planning is relatively rare. [...] Read more.
The depth of water is of great significance to the safe navigation of unmanned surface vehicles (USV)in shallow waters, such as islands and reefs. How to consider the influence of depth on the safety of USV navigation and path planning is relatively rare. Under the condition of ocean disturbance, the hydrodynamic characteristics of unmanned surface vehicles will affect its draft and depth safety. In this paper, the hydrodynamic model of unmanned surface vehicles is analyzed, and a water depth risk level A* algorithm (WDRLA*) is proposed. According to the depth point of the electronic navigation chart (ENC), the gridding depth can be obtained by spline function interpolation. The WDRLA* algorithm is applied to plan the path, which takes hydrodynamic characteristics and navigation errors into account. It is compared with the traditional A* shortest path and safest path. The simulation results show that the WDRLA* algorithm can reduce the depth hazard of the shortest path and ensure the safety of navigation. Full article
(This article belongs to the Special Issue Mobile Robots Navigation)
Show Figures

Figure 1

16 pages, 13753 KiB  
Article
Multi-Robot Path Planning Method Using Reinforcement Learning
by Hyansu Bae, Gidong Kim, Jonguk Kim, Dianwei Qian and Sukgyu Lee
Appl. Sci. 2019, 9(15), 3057; https://doi.org/10.3390/app9153057 - 29 Jul 2019
Cited by 131 | Viewed by 12295
Abstract
This paper proposes a noble multi-robot path planning algorithm using Deep q learning combined with CNN (Convolution Neural Network) algorithm. In conventional path planning algorithms, robots need to search a comparatively wide area for navigation and move in a predesigned formation under a [...] Read more.
This paper proposes a noble multi-robot path planning algorithm using Deep q learning combined with CNN (Convolution Neural Network) algorithm. In conventional path planning algorithms, robots need to search a comparatively wide area for navigation and move in a predesigned formation under a given environment. Each robot in the multi-robot system is inherently required to navigate independently with collaborating with other robots for efficient performance. In addition, the robot collaboration scheme is highly depends on the conditions of each robot, such as its position and velocity. However, the conventional method does not actively cope with variable situations since each robot has difficulty to recognize the moving robot around it as an obstacle or a cooperative robot. To compensate for these shortcomings, we apply Deep q learning to strengthen the learning algorithm combined with CNN algorithm, which is needed to analyze the situation efficiently. CNN analyzes the exact situation using image information on its environment and the robot navigates based on the situation analyzed through Deep q learning. The simulation results using the proposed algorithm shows the flexible and efficient movement of the robots comparing with conventional methods under various environments. Full article
(This article belongs to the Special Issue Mobile Robots Navigation)
Show Figures

Figure 1

16 pages, 3843 KiB  
Article
Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer
by Albina Kamalova, Sergey Navruzov, Dianwei Qian and Suk Gyu Lee
Appl. Sci. 2019, 9(14), 2931; https://doi.org/10.3390/app9142931 - 22 Jul 2019
Cited by 20 | Viewed by 5653
Abstract
In this paper, we used multi-objective optimization in the exploration of unknown space. Exploration is the process of generating models of environments from sensor data. The goal of the exploration is to create a finite map of indoor space. It is common practice [...] Read more.
In this paper, we used multi-objective optimization in the exploration of unknown space. Exploration is the process of generating models of environments from sensor data. The goal of the exploration is to create a finite map of indoor space. It is common practice in mobile robotics to consider the exploration as a single-objective problem, which is to maximize a search of uncertainty. In this study, we proposed a new methodology of exploration with two conflicting objectives: to search for a new place and to enhance map accuracy. The proposed multiple-objective exploration uses the Multi-Objective Grey Wolf Optimizer algorithm. It begins with the initialization of the grey wolf population, which are waypoints in our multi-robot exploration. Once the waypoint positions are set in the beginning, they stay unchanged through all iterations. The role of updating the position belongs to the robots, which select the non-dominated waypoints among them. The waypoint selection results from two objective functions. The performance of the multi-objective exploration is presented. The trade-off among objective functions is unveiled by the Pareto-optimal solutions. A comparison with other algorithms is implemented in the end. Full article
(This article belongs to the Special Issue Mobile Robots Navigation)
Show Figures

Figure 1

22 pages, 3017 KiB  
Article
Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm
by Suping Zhao, Zhanxia Zhu and Jianjun Luo
Appl. Sci. 2019, 9(11), 2226; https://doi.org/10.3390/app9112226 - 30 May 2019
Cited by 19 | Viewed by 3035
Abstract
This work addresses the multitask-based trajectory-planning problem (MTTP) for space robotics, which is an emerging application of successively executing tasks in assembly of the International Space Station. The MTTP is transformed into a parameter-optimization problem, where piecewise continuous-sine functions are employed to depict [...] Read more.
This work addresses the multitask-based trajectory-planning problem (MTTP) for space robotics, which is an emerging application of successively executing tasks in assembly of the International Space Station. The MTTP is transformed into a parameter-optimization problem, where piecewise continuous-sine functions are employed to depict the joint trajectories. An improved genetic algorithm (IGA) is developed to optimize the unknown parameters. In the IGA, each chromosome consists of three parts, namely the waypoint sequence, the sequence of the joint configurations, and a special value for the depiction of the joint trajectories. Numerical simulations, including comparisons with two other approaches, are developed to test IGA validity. Full article
(This article belongs to the Special Issue Mobile Robots Navigation)
Show Figures

Figure 1

27 pages, 59577 KiB  
Article
A Robotic Deburring Methodology for Tool Path Planning and Process Parameter Control of a Five-Degree-of-Freedom Robot Manipulator
by Wanjin Guo, Ruifeng Li, Yaguang Zhu, Tong Yang, Rui Qin and Zhixin Hu
Appl. Sci. 2019, 9(10), 2033; https://doi.org/10.3390/app9102033 - 17 May 2019
Cited by 8 | Viewed by 6382
Abstract
Industrial robotics is a continuously developing domain, as industrial robots have demonstrated to possess benefits with regard to robotic automation solutions in the industrial automation field. In this article, a new robotic deburring methodology for tool path planning and process parameter control is [...] Read more.
Industrial robotics is a continuously developing domain, as industrial robots have demonstrated to possess benefits with regard to robotic automation solutions in the industrial automation field. In this article, a new robotic deburring methodology for tool path planning and process parameter control is presented for a newly developed five-degree-of-freedom hybrid robot manipulator. A hybrid robot manipulator with dexterous manipulation and two experimental platforms of robot manipulators are presented. A robotic deburring tool path planning method is proposed for the robotic deburring tool position and orientation planning and the robotic layered deburring planning. Also, a robotic deburring process parameter control method is proposed based on fuzzy control. Furthermore, a dexterous manipulation verification experiment is conducted to demonstrate the dexterous manipulation and the orientation reachability of the robot manipulator. Additionally, two robotic deburring experiments are conducted to verify the effectiveness of the two proposed methods and demonstrate the highly efficient and dexterous manipulation and deburring capacity of the robot manipulator. Full article
(This article belongs to the Special Issue Mobile Robots Navigation)
Show Figures

Figure 1

18 pages, 4999 KiB  
Article
Speed Optimization for Incremental Updating of Grid-Based Distance Maps
by Long Qin, Yue Hu, Quanjun Yin and Junjie Zeng
Appl. Sci. 2019, 9(10), 2029; https://doi.org/10.3390/app9102029 - 16 May 2019
Cited by 3 | Viewed by 2743
Abstract
In the context of robotics and game AI, grid-based Distance Maps (DMs) are often used to fulfill collision checks by providing each traversable cell maximal clearance to its closest obstacle. A key challenge for DMs’ application is how to improve the efficiency of [...] Read more.
In the context of robotics and game AI, grid-based Distance Maps (DMs) are often used to fulfill collision checks by providing each traversable cell maximal clearance to its closest obstacle. A key challenge for DMs’ application is how to improve the efficiency of updating the distance values when cell states are changed (i.e., changes caused by newly inserted or removed obstacles). To this end, this paper presents a novel algorithm to speed up the construction of DMs on planar, eight-connected grids. The novelty of our algorithm, Canonical Ordering Dynamic Brushfire (CODB), lies in two aspects: firstly, it only updates those cells which are affected by the changes; secondly, it employs the strategy of Canonical Ordering from the fast path planning community to guide the direction of the update; therefore, the construction requires much fewer cell visits and less computation costs compared to previous algorithms. Furthermore, we propose algorithms to compute DM-based subgoal graphs. Such a spatial representation can be used to provide high-level, collision-free roadmaps for agents with certain safety radius to engage fast and rational path planning tasks. We present our algorithm both intuitively and through pseudocode, compare it to competing algorithms in simulated scenarios, and demonstrate its usefulness for real-time path planning tasks. Full article
(This article belongs to the Special Issue Mobile Robots Navigation)
Show Figures

Figure 1

23 pages, 6650 KiB  
Article
Framework for Fast Experimental Testing of Autonomous Navigation Algorithms
by Miguel Á. Muñoz–Bañón, Iván del Pino, Francisco A. Candelas and Fernando Torres
Appl. Sci. 2019, 9(10), 1997; https://doi.org/10.3390/app9101997 - 15 May 2019
Cited by 12 | Viewed by 5656
Abstract
Research in mobile robotics requires fully operative autonomous systems to test and compare algorithms in real-world conditions. However, the implementation of such systems remains to be a highly time-consuming process. In this work, we present an robot operating system (ROS)-based navigation framework that [...] Read more.
Research in mobile robotics requires fully operative autonomous systems to test and compare algorithms in real-world conditions. However, the implementation of such systems remains to be a highly time-consuming process. In this work, we present an robot operating system (ROS)-based navigation framework that allows the generation of new autonomous navigation applications in a fast and simple way. Our framework provides a powerful basic structure based on abstraction levels that ease the implementation of minimal solutions with all the functionalities required to implement a whole autonomous system. This approach helps to keep the focus in any sub-problem of interest (i.g. localization or control) while permitting to carry out experimental tests in the context of a complete application. To show the validity of the proposed framework we implement an autonomous navigation system for a ground robot using a localization module that fuses global navigation satellite system (GNSS) positioning and Monte Carlo localization by means of a Kalman filter. Experimental tests are performed in two different outdoor environments, over more than twenty kilometers. All the developed software is available in a GitHub repository. Full article
(This article belongs to the Special Issue Mobile Robots Navigation)
Show Figures

Figure 1

18 pages, 8523 KiB  
Article
SURF-BRISK–Based Image Infilling Method for Terrain Classification of a Legged Robot
by Yaguang Zhu, Chaoyu Jia, Chao Ma and Qiong Liu
Appl. Sci. 2019, 9(9), 1779; https://doi.org/10.3390/app9091779 - 29 Apr 2019
Cited by 7 | Viewed by 2652
Abstract
In this study, we propose adaptive locomotion for an autonomous multilegged walking robot, an image infilling method for terrain classification based on a combination of speeded up robust features, and binary robust invariant scalable keypoints (SURF-BRISK). The terrain classifier is based on the [...] Read more.
In this study, we propose adaptive locomotion for an autonomous multilegged walking robot, an image infilling method for terrain classification based on a combination of speeded up robust features, and binary robust invariant scalable keypoints (SURF-BRISK). The terrain classifier is based on the bag-of-words (BoW) model and SURF-BRISK, both of which are fast and accurate. The image infilling method is used for identifying terrain with obstacles and mixed terrain; their features are magnified to help with recognition of different complex terrains. Local image infilling is used to improve low accuracy caused by obstacles and super-pixel image infilling is employed for mixed terrain. A series of experiments including classification of terrain with obstacles and mixed terrain were conducted and the obtained results show that the proposed method can accurately identify all terrain types and achieve adaptive locomotion. Full article
(This article belongs to the Special Issue Mobile Robots Navigation)
Show Figures

Figure 1

17 pages, 1544 KiB  
Article
Modeling and Analysis on Energy Consumption of Hydraulic Quadruped Robot for Optimal Trot Motion Control
by Kun Yang, Xuewen Rong, Lelai Zhou and Yibin Li
Appl. Sci. 2019, 9(9), 1771; https://doi.org/10.3390/app9091771 - 28 Apr 2019
Cited by 24 | Viewed by 3805
Abstract
Energy consumption is an important performance index of quadruped robots. In this paper, the energy consumptions of the quadruped robot SCalf with a trot gait under different gait parameters are analyzed. Firstly, the kinematics and dynamics models of the robot are established. Then, [...] Read more.
Energy consumption is an important performance index of quadruped robots. In this paper, the energy consumptions of the quadruped robot SCalf with a trot gait under different gait parameters are analyzed. Firstly, the kinematics and dynamics models of the robot are established. Then, an energy model including the mechanical power and heat rate is proposed. To obtain the energy consumption, a cubic spline interpolation foot trajectory is used, and the feet forces are calculated by using the minimization of norm of the foot force method. Moreover, an energetic criterion measuring the energy cost is defined to evaluate the motion. Finally, the gait parameters such as step height, step length, standing height, gait cycle, and duty cycle that influence the energy consumption are studied, which could provide a theoretical basis for parameter optimization and motion control of quadruped robots. Full article
(This article belongs to the Special Issue Mobile Robots Navigation)
Show Figures

Figure 1

14 pages, 7358 KiB  
Article
Smart Obstacle Avoidance Using a Danger Index for a Dynamic Environment
by Jiubo Sun, Guoliang Liu, Guohui Tian and Jianhua Zhang
Appl. Sci. 2019, 9(8), 1589; https://doi.org/10.3390/app9081589 - 17 Apr 2019
Cited by 27 | Viewed by 4202
Abstract
The artificial potential field approach provides a simple and effective motion planner for robot navigation. However, the traditional artificial potential field approach in practice can have a local minimum problem, i.e., the attractive force from the target position is in the balance with [...] Read more.
The artificial potential field approach provides a simple and effective motion planner for robot navigation. However, the traditional artificial potential field approach in practice can have a local minimum problem, i.e., the attractive force from the target position is in the balance with the repulsive force from the obstacle, such that the robot cannot escape from this situation and reach the target. Moreover, the moving object detection and avoidance is still a challenging problem with the current artificial potential field method. In this paper, we present an improved version of the artificial potential field method, which uses a dynamic window approach to solve the local minimum problem and define a danger index in the speed field for moving object avoidance. The new danger index considers not only the relative distance between the robot and the obstacle, but also the relative velocity according to the motion of the moving objects. In this way, the robot can find an optimized path to avoid local minimum and moving obstacles, which is proved by our experimental results. Full article
(This article belongs to the Special Issue Mobile Robots Navigation)
Show Figures

Figure 1

17 pages, 1125 KiB  
Article
A New Coverage Flight Path Planning Algorithm Based on Footprint Sweep Fitting for Unmanned Aerial Vehicle Navigation in Urban Environments
by Abdul Majeed and Sungchang Lee
Appl. Sci. 2019, 9(7), 1470; https://doi.org/10.3390/app9071470 - 8 Apr 2019
Cited by 32 | Viewed by 5274
Abstract
This paper presents a new coverage flight path planning algorithm that finds collision-free, minimum length and flyable paths for unmanned aerial vehicle (UAV) navigation in three-dimensional (3D) urban environments with fixed obstacles for coverage missions. The proposed algorithm significantly reduces computational time, number [...] Read more.
This paper presents a new coverage flight path planning algorithm that finds collision-free, minimum length and flyable paths for unmanned aerial vehicle (UAV) navigation in three-dimensional (3D) urban environments with fixed obstacles for coverage missions. The proposed algorithm significantly reduces computational time, number of turns, and path overlapping while finding a path that passes over all reachable points of an area or volume of interest by using sensor footprints’ sweeps fitting and a sparse waypoint graph in the pathfinding process. We devise a novel footprints’ sweep fitting method considering UAV sensor footprint as coverage unit in the free spaces to achieve maximal coverage with fewer and longer footprints’ sweeps. After footprints’ sweeps fitting, the proposed algorithm determines the visiting sequence of footprints’ sweeps by formulating it as travelling salesman problem (TSP), and ant colony optimization (ACO) algorithm is employed to solve the TSP. Furthermore, we generate a sparse waypoint graph by connecting footprints’ sweeps’ endpoints to obtain a complete coverage flight path. The simulation results obtained from various scenarios fortify the effectiveness of the proposed algorithm and verify the aforementioned claims. Full article
(This article belongs to the Special Issue Mobile Robots Navigation)
Show Figures

Figure 1

19 pages, 2754 KiB  
Article
Integrating a Path Planner and an Adaptive Motion Controller for Navigation in Dynamic Environments
by Junjie Zeng, Long Qin, Yue Hu, Quanjun Yin and Cong Hu
Appl. Sci. 2019, 9(7), 1384; https://doi.org/10.3390/app9071384 - 2 Apr 2019
Cited by 16 | Viewed by 3578
Abstract
Since an individual approach can hardly navigate robots through complex environments, we present a novel two-level hierarchical framework called JPS-IA3C (Jump Point Search improved Asynchronous Advantage Actor-Critic) in this paper for robot navigation in dynamic environments through continuous controlling signals. Its global planner [...] Read more.
Since an individual approach can hardly navigate robots through complex environments, we present a novel two-level hierarchical framework called JPS-IA3C (Jump Point Search improved Asynchronous Advantage Actor-Critic) in this paper for robot navigation in dynamic environments through continuous controlling signals. Its global planner JPS+ (P) is a variant of JPS (Jump Point Search), which efficiently computes an abstract path of neighboring jump points. These nodes, which are seen as subgoals, completely rid Deep Reinforcement Learning (DRL)-based controllers of notorious local minima. To satisfy the kinetic constraints and be adaptive to changing environments, we propose an improved A3C (IA3C) algorithm to learn the control policies of the robots’ local motion. Moreover, the combination of modified curriculum learning and reward shaping helps IA3C build a novel reward function framework to avoid learning inefficiency because of sparse reward. We additionally strengthen the robots’ temporal reasoning of the environments by a memory-based network. These improvements make the IA3C controller converge faster and become more adaptive to incomplete, noisy information caused by partial observability. Simulated experiments show that compared with existing methods, this JPS-IA3C hierarchy successfully outputs continuous commands to accomplish large-range navigation tasks at shorter paths and less time through reasonable subgoal selection and rational motions. Full article
(This article belongs to the Special Issue Mobile Robots Navigation)
Show Figures

Figure 1

30 pages, 4530 KiB  
Article
Evaluation of Clustering Methods in Compression of Topological Models and Visual Place Recognition Using Global Appearance Descriptors
by Sergio Cebollada, Luis Payá, Walterio Mayol and Oscar Reinoso
Appl. Sci. 2019, 9(3), 377; https://doi.org/10.3390/app9030377 - 22 Jan 2019
Cited by 17 | Viewed by 2906
Abstract
This paper presents an extended study about the compression of topological models of indoor environments. The performance of two clustering methods is tested in order to know their utility both to build a model of the environment and to solve the localization task. [...] Read more.
This paper presents an extended study about the compression of topological models of indoor environments. The performance of two clustering methods is tested in order to know their utility both to build a model of the environment and to solve the localization task. Omnidirectional images are used to create the compact model, as well as to estimate the robot position within the environment. These images are characterized through global appearance descriptors, since they constitute a straightforward mechanism to build a compact model and estimate the robot position. To evaluate the goodness of the proposed clustering algorithms, several datasets are considered. They are composed of either panoramic or omnidirectional images captured in several environments, under real operating conditions. The results confirm that compression of visual information contributes to a more efficient localization process through saving computation time and keeping a relatively good accuracy. Full article
(This article belongs to the Special Issue Mobile Robots Navigation)
Show Figures

Figure 1

19 pages, 7527 KiB  
Article
Mixed-Degree Cubature H Information Filter-Based Visual-Inertial Odometry
by Chunlin Song, Xiaogang Wang and Naigang Cui
Appl. Sci. 2019, 9(1), 56; https://doi.org/10.3390/app9010056 - 24 Dec 2018
Cited by 3 | Viewed by 2376
Abstract
Visual–inertial odometry is an effective system for mobile robot navigation. This article presents an egomotion estimation method for a dual-sensor system consisting of a camera and an inertial measurement unit (IMU) based on the cubature information filter and H filter. The intensity [...] Read more.
Visual–inertial odometry is an effective system for mobile robot navigation. This article presents an egomotion estimation method for a dual-sensor system consisting of a camera and an inertial measurement unit (IMU) based on the cubature information filter and H filter. The intensity of the image was used as the measurement directly. The measurements from the two sensors were fused with a hybrid information filter in a tightly coupled way. The hybrid filter used the third-degree spherical-radial cubature rule in the time-update phase and the fifth-degree spherical simplex-radial cubature rule in the measurement-update phase for numerical stability. The robust H filter was combined into the measurement-update phase of the cubature information filter framework for robustness toward non-Gaussian noises in the intensity measurements. The algorithm was evaluated on a common public dataset and compared to other visual navigation systems in terms of absolute and relative accuracy. Full article
(This article belongs to the Special Issue Mobile Robots Navigation)
Show Figures

Figure 1

Back to TopTop