Mobile Robot Localization Method Research Articles

A Synchronous Localization Method for Multiple Mobile Robots Based on Visual SLAM Algorithm in the Context of the Internet of Things

Abstract Aiming at the problem that traditional methods cannot meet the problem of synchronous positioning of multi-mobile robots in different motion states, a synchronous positioning method of multi-mobile robots based on visual simultaneous localization and mapping (SLAM) algorithm is proposed. Analyze the general model of SLAM problem, realize visual SLAM feature detection through scale invariant feature transform algorithm, and match image features based on active vision. A multi-mobile robot motion state estimation model is constructed, an octree model is used to construct a multi-robot synchronous localization map, and the camera pose tracking is realized by using the oriented fast accelerated segment test and rotated binary robust independent elementary features points that are not in the target bounding box. The three-dimensional points in the environment recovered by visual SLAM separate the background target and the moving target, and match the corresponding target in the previous frame through the feature to realize the synchronous positioning of the multi-mobile robot. The experimental results show that the method can realize the synchronous positioning of multi-robots under the state of multi-robot linear motion, curvilinear motion and mixed motion, and the positioning accuracy is high.

Autonomous Localization and Mapping Method of Mobile Robot in Underground Coal Mine Based on Edge Computing

When applying visual SLAM systems to underground coal mines, several challenges arise. First, there are non-ideal texture areas in the scene, which make feature extraction and matching difficult and reduce the accuracy of positioning and mapping. Second, the limited computing resources of mobile robots prevent the real-time execution of complex algorithms. To address these challenges, this paper proposes an edge computing-based SLAM system that fuses point and line features. The visual odometer of point and line feature fusion solves the problem of insufficient feature extraction in texture sparse areas and incorrect feature matching in texture repetitive areas, thereby improving the accuracy of visual positioning and mapping. The distributed deployment strategy of edge computing enables the algorithm to be executed in real-time on the underground coal mine mobile robot. The experiment demonstrated that using the visual odometer method with ORB-SLAM 2 reduced the absolute trajectory error by 8.87% in dense repetitive texture areas and 9.96% in low texture areas.

A Dynamic Window-Based UWB-Odometer Fusion Approach for Indoor Positioning

The positioning system based on ultrawideband (UWB) can achieve centimeter-level positioning accuracy, which is now widely used for indoor positioning. However, UWB is subject to multipath effects and nonline-of-sight (NLOS), which can cause positioning errors. And a UWB positioning system with a minimum of three anchors is costly and difficult to deploy, which is not an economical option. This article presents a low-cost UWB-odometer fusion method for mobile robot localization, which enables global localization using only one UWB anchor. To improve the positioning accuracy and eliminate the influence of cumulative odometer error, we propose a dynamic window-based particle filter (DWBPF). It solves the problem of particle convergence and track loss. It also adaptively modifies the score weights according to the power difference of the UWB to achieve a more robust and accurate positioning. The experimental result shows that the system has a high positioning accuracy of 0.061 m.

Improved LiDAR Localization Method for Mobile Robots Based on Multi-Sensing

In this paper, we propose a localization method applicable to 3D LiDAR by improving the LiDAR localization algorithm, such as AMCL (Adaptive Monte Carlo Localization). The method utilizes multiple sensing information, including 3D LiDAR, IMU and the odometer, and can be used without GNSS. Firstly, the wheel speed odometer and IMU data of the mobile robot are multi-source fused by EKF (Extended Kalman Filter), and the sensor data obtained after multi-source fusion are used as the motion model to participate in the positional prediction of the particle set in AMCL to obtain the initial positioning information of the mobile robot. Then, the position pose difference values output by AMCL at adjacent moments are substituted into the PL-ICP algorithm as the initial position pose transformation matrix, and the 3D laser point cloud is aligned with the nonlinear system using the PL-ICP algorithm. The three-dimensional laser odometer is obtained by LM (Levenberg--Marquard) iterative solution in the PL-ICP algorithm. Finally, the initial position pose output by AMCL is corrected by the three-dimensional laser odometer, and the AMCL particles are weighted and sampled to output the final positioning result of the mobile robot. Through simulation and practical experiments, it is verified that the improved AMCL algorithm has higher positioning accuracy and stability compared to the AMCL algorithm.

The Localization Methods in Mobile Robot Navigation Technology: A Patent Review

Background: The mobile robot navigation technology includes two key issues: the localization and the path planning, of which the localization is the most basic part of mobile robot navigation, because the target point is meaningful only when mobile robot's pose is clarified, and path planning can be done on this basis. Therefore, it is of great significance to have a good localization method for mobile robots. Objective: This study aimed to summarize the existing localization methods in mobile robot navigation technology and introduce their classifications, characteristics, as well as the stage and trends of development. Methods: A retrospective review of various patents and literature related to localization methods in mobile robot navigation technology was performed. The characteristics, differences, and applications of different localization methods were also introduced. Results: The existing localization methods were analyzed and compared, and their typical characteristics are summarized. The main applications, as well as the pros and cons in the current development stage, were summarized and analyzed. In addition, the development trend of patents related to positioning methods is also discussed. Conclusion: This paper summarized three localization methods and provided numerous cases. It can be seen that most of the existing localization methods are dedicated to improving the positioning accuracy and efficiency of mobile robots, while there is less research on the robustness of mobile robot localization, which is not conducive to the research on robots with the unknown working environment and long moving distances. Therefore, related research patents and documents should be proposed. Current and future development trends are also looking forward.

A Mobile Robot Localization Method Based on Polar Scan Matching and Adaptive Niching Chaos Optimization Algorithm

A Mobile Robot Localization Method Based on Polar Scan Matching and Adaptive Niching Chaos Optimization Algorithm

A fast gamma-ray source localization method for mobile robots

A method for fast estimation of radioactive source parameters is presented. Full use was made of multiple continuous count values, combined with triangulation with four-point sampling method to solve high-dimensional optimization to obtain initialized estimates, which converge to maximum likelihood estimation using an improved hill-climbing algorithm. Cobalt-60 search experiments were conducted in the laboratory using a mobile robot carrying a NaI(TI) detector. Experimental results showed that the method can significantly improve the computational speed.

Mobile Robot Localization Considering Uncertainty of Depth Regression From Camera Images

This letter presents a mobile robot localization method that uses depth regression from camera images. In this work, we use convolutional neural networks to regress the depth from the camera images. However, the depth regression results contain uncertainty, which must be resolved to stably perform localization with the depth regression results. This letter proposes a novel probabilistic model that enables the handling of the uncertainty of the depth regression results while localizing the robot pose. By handling the uncertainty, inaccurate depth regression results can be ignored, and localization robustness can be increased. We compare the proposed method with two traditional methods used in particle-filter-based localization that do not handle depth regression uncertainty. Comparison experiments are performed using two types of datasets: a manually created dataset using only a visual inertial odometry sensor, and the KITTI odometry dataset. Results show that the proposed method can track the robot pose even though the depth regression results are inaccurate and can increase localization accuracy without increasing memory and computational costs.

Mobile Robot Indoor Localization Using Color-Coded Beacons and a Depth Camera

This paper aims to design and implement an indoor localization method for mobile robots based on trilateration technique, Color Code signatures (CCs) as artificial passive beacons, and a depth camera. The artificial passive beacons are designed as three disks positioned one on top of the other’s with different sizes and colors (primary colors only, RGB). The three disks share the same center. The designed beacons are placed on the ceiling to be visible from most locations within the room. The robot's model, controller, and localization method are implemented and evaluated inside the CoppeliaSim environment. The simulation results show that the CCs detecting algorithm enables the robot to discover the designed beacons, achieve an accurate localization and reach the target

Multispectral Visual Odometry Using SVSF for Mobile Robot Localization

In this paper, we propose a novel method for mobile robot localization and navigation based on multispectral visual odometry (MVO). The proposed approach consists in combining visible and infrared images to localize the mobile robot under different conditions (day, night, indoor and outdoor). The depth image acquired by the Kinect sensor is very sensitive for IR luminosity, which makes it not very useful for outdoor localization. So, we propose an efficient solution for the aforementioned Kinect limitation based on three navigation modes: indoor localization based on RGB/depth images, night localization based on depth/IR images and outdoor localization using multispectral stereovision RGB/IR. For automatic selection of the appropriate navigation modes, we proposed a fuzzy logic controller based on images’ energies. To overcome the limitation of the multimodal visual navigation (MMVN) especially during navigation mode switching, a smooth variable structure filter (SVSF) is implemented to fuse the MVO pose with the wheel odometry (WO) pose based on the variable structure theory. The proposed approaches are validated with success experimentally for trajectory tracking using the mobile robot (Pioneer P3-AT).

CARE: A Collision-Aware Mobile Robot Navigation in Grid Environment using Improved Breadth First Search

In recent years, there is an increasing interest in designing autonomous vehicles such as Mobile Robots. However, one of the fundamental needs of Mobile Robots is a collision-free navigation with an optimal path from the source to the destination. In this paper, a collision-free low-complexity Mobile Robot navigation scheme called Collision Aware Mobile Robot navigation in Grid-Environment is designed. The proposed scheme uses the Radio Frequency based Identification method for Mobile Robot localization, the hybrid approach for the path planning, and a predefined decision table for the navigation. The algorithms are implemented in two stages, construction of virtual world and generation of optimal shortest path. The algorithms’ performance is analyzed in grid-based environment of size 5×5,20×20,35×35, and 50 × 50 with four different cases. It is observed that for the environment with no obstacles, the robot explores fewer cells in finding the shortest path. However, the number of turning in the shortest path is always less in the environment with some obstacles than in the case of whole virtual world exploration. The performance results show the effectiveness of the proposed scheme for Mobile Robot navigation in the grid environment.

A UWB-Based Low-Cost and Precise Localization Method for Mobile Robots

The need for automating cargo management in a manufacture environment is urgent. Recent advance in big data and artificial intelligence areas has made it possible. As we known, most cargoes are transported using autonomously guided vehicles or manpower. Many autonomously guided vehicles follow paths that are predefined and usually marked by labels that can be easily found by vehicles. The use of predefined path for vehicle navigation greatly limits the deployment of autonomously guided vehicles. A few vehicles use high accuracy Light Laser Detection and Ranging for sensing and guidance. In this paper, a low-cost and precise method for mobile robots has been developed. Considering the high nonlinearity of UWB ranging data, a least square method is used to find optimal locations and a gradient decent approach is applied for fast convergence to the optimal results. The proposed method is also able to diagnose the ultra-wide band data and will throw away any data corrupted by noise. Therefore, the robustness is obtained.

RoomSLAM: Simultaneous Localization and Mapping With Objects and Indoor Layout Structure

This article presents RoomSLAM, a Simultaneous Localization and Mapping (SLAM) method for mobile robots in indoor environments where environments are modeled by points and quadrilaterals in 2D space. Points represent positions of semantic objects whereas quadrilaterals approximate the structural layout of the environment, namely rooms. The benefit of such modeling is threefold. Firstly, rooms are a logical way to partition a graph in large-scale SLAM. Secondly, rooms and objects reduce search space in data association. Lastly, the model contains a higher level of semantic, which is beneficial to autonomous robots whenever inter-room navigation is needed. The method was evaluated with two public datasets and the results were compared to those of ORBSLAM and RGBDSLAM.

Robust Localization Method of Autonomous Mobile Robot for Occluded LiDAR Data

Robust Localization Method of Autonomous Mobile Robot for Occluded LiDAR Data

옥외용 이동로봇의 확장 칼만 필터 기반 3차원 위치평가 방법

This paper proposes a 3D localization method for an outdoor mobile robot. This method assesses the 3D position including the altitude information, which is impossible in the existing 2D localization method. In this method, the 3D position of the robot is predicted using an encoder and an inclination sensor. The predicted position is fused with the position information obtained from the DGPS and the digital compass using extended kalman filter to evaluate the 3D position of the robot. The experimental results showed that the proposed method can effectively evaluate the 3D position of the robot in a sloping environment. Moreover, this method was found to be more effective than the conventional 2D localization method even in the evaluation of the plane position where altitude information is unnecessary.

Indoor Localization for Skid-Steering Mobile Robot by Fusing Encoder, Gyroscope, and Magnetometer

This paper presents a novel indoor localization method for skid-steering mobile robot by fusing the readings from encoder, gyroscope, and magnetometer which can be read as an enhanced dead-reckoning localization method. Compared with the traditional dead-reckoning localization method implemented by encoder only, the accuracy and reliability can be improved significantly in spite of the price of slightly higher cost in digital devices. The proposed strategy consists mainly of an orientation algorithm and a localization algorithm. First, realizing that gyroscope is barely affected by magnetic field and magnetometer-based orientation has no cumulative error, a novel orientation algorithm, based on the self-tuning Kalman filter coupled with a gross error recognizer, is developed. This orientation algorithm can be applied to determine the robot heading angle in the situation with abundant ferromagnetic materials. Second, based on the orientation algorithm we have proposed, a novel localization algorithm is designed by decomposing the robot motion into uniform linear motion and uniform circular motion. The effectiveness of the proposed indoor localization method is verified via the real-world experiment using a tracked mobile robot developed in our laboratory.

Localization Method for an Autonomous Mobile Robot Using Neural Network to Merge Multiple Localization Methods

Many localization methods have been proposed for an autonomous mobile robot and many studies focus on enhancement of capability of each means. Monte Carlo localization such as particle filter has good robustness and widely used for mobile robots but lacks precise estimation in a certain environment. ICP(Iterative Closest Points) matching has good performance about estimation precision when there are many good features such as many straight walls but lacks robustness if there are few distinguish feature points around the robot. In this paper we propose a fusion strategy using neural network which can be applied to many fields in a building such as an indoor public space. Simulation results for 3 characteristic environments show effectiveness of the proposed strategy.

Establishing a Localization Method for Mobile Robots to Support Bridge Inspection

Establishing a Localization Method for Mobile Robots to Support Bridge Inspection

Proposal of localization method of mobile robot using surveillance camera and accuracy evaluation in stopped state

Proposal of localization method of mobile robot using surveillance camera and accuracy evaluation in stopped state

Robust stairway-detection and localization method for mobile robots using a graph-based model and competing initializations

One of the major challenges for mobile robots in human-shaped environments is navigating stairways. This study presents a method for accurately detecting, localizing, and estimating the characteristics of stairways using point cloud data. The main challenge is the wide variety of different structures and shapes of stairways. This challenge is often aggravated by an unfavorable position of the sensor, which leaves large parts of the stairway occluded. This can be further aggravated by sparse point data. We overcome these difficulties by introducing a three-dimensional graph-based stairway-detection method combined with competing initializations. The stairway graph characterizes the general structural design of stairways in a generic way that can be used to describe a large variety of different stairways. By using multiple ways to initialize the graph, we can robustly detect stairways even if parts of the stairway are occluded. Furthermore, by letting the initializations compete against each other, we find the best initialization that accurately describes the measured stairway. The detection algorithm utilizes a plane-based approach. We also investigate different planar segmentation algorithms and experimentally compare them in an application-orientated manner. Our system accurately detects and estimates the stairway parameters with an average error of only for a variety of stairways including ascending, descending, and spiral stairways. Our method works robustly with different depth sensors for either small- or large-scale environments and for dense and sparse point cloud data. Despite this generality, our system’s accuracy is higher than most state-of-the-art stairway-detection methods.