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

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).

Localization is one of many issues in mobile robot study. Localization is an essential ability for mobile robot to determine its location, so that it can plan a movement and go to a desired location. The mutual method for mobile robot localization is using a particle filter. High computation needs in particle filter is one of problem in particle filtering to get accurate location. The paper proposes a low computational mobile robot localization using a particle filter. It uses two methods: local localization using a dead reckoning method and global localization using a landmark-based vision sensor. Simulation results show that the proposed method provides a good estimation on the mobile robot position and orientation.

Mobile robot localization based on omnicamera

This paper proposes a new localization method for indoor mobile robots. Using two cameras and one laser range finder on board a TRC mobile robot, the initial position and pose of the robot can be obtained by multisensor fusion and scene matching based on geometric hashing. No correspondence calculation and special pattern recognition are needed during the scene matching. This localization method can be implemented in five stages: 1) Model the indoor environment. Some selected indoor environment features are firstly modeled off-line into hashing tables. 2) Perform system calibration and information fusion from two cameras and the range finder. 3) Extract the vertical edge points corresponding to the horizontal scanning plane of the 2D laser range finder from the scene images and transform them into geometric invariants. 4) Perform scene matching and matching verification by geometric hashing and model back- projection method respectively. 5) Perform position and pose estimation by a least square fit method. Experimental results show that the accuracy and reliability of this localization method ar quite high.

Owing to the presence of multiple obstacles or changes in the positions of surroundings objects, the localization of a mobile robot using only natural landmarks is difficult in indoor environments. Consequently, in these environments, the use of artificial landmarks to achieve robust localization is necessary. To this end, we propose a global localization method for mobile robots, using reflectors as indistinguishable artificial landmarks, not the manmade signs have a certain pattern. In previous studies, it has been shown that localization is possible using information about the distance between indistinguishable artificial landmarks. We applied this principle in a real-world situation with many obstacles or in a dynamic environment and confirmed that robust, high-accuracy global localization is possible.

In this paper, a novel indoor localization method for mobile robot working in complex environment is presented. Natural features are obtained from 3D rebuilding image sequences. The geometrical relationship between the natural feature points and the homologous points of current view are found to locate the positions of a moving robot. Experiment result shows that this novel localization method is reliable and effective.

Simultaneous Localizatoin and Mapping (SLAM) is an active area of robot research and the location technology of mobile robots is a very critical issue in the filed of SLAM. Low cost and high performance can not be balanced in commercial robots and we plan to achieve better performance with low-cost sensors. This paper focuses on the study of complex indoor environment and a new method of point cloud matching and low-cost mobile robot localization is presented. Firstly, we change the data within a range of distance from laser to image by building grid maps and we consider each map has no scaling relation to others. Second, fast Fourier transformation is used to get the rotation angle. In order to get translation parameters, one-dimensional Fourier transformation is used to horizontal projection and vertical projection of map. We can build maps based on positioning results. Finally, we perform comparative experiments with other common methods.

This paper presents a global localization method for mobile robots based on the geometric information of structured indoor environments. With a global/local point cloud and projection map, lines are extracted from the projection maps using Hough transform. According to the directions of the obtained lines, the orientations of projection maps and point clouds are normalized. Next, the template matching algorithm is applied to the normalized global and local projection maps. Once coarse localization is completed, final accurate localization is achieved using the Iterative Closest Points (ICP) algorithm. Experimental results on several point clouds show that the proposed method can achieve high localization accuracy in real-time. The proposed method can be used for other global localization applications in structured environments.

The hybrid localization method of straight line matching, corner matching and odometry is proposed to resolve the difficult localization problems of mobile robot operating in family indoor environment. Localization of robot is a challenge problem which involves of determining robot positioning relative to objects in the environment. The system is implemented using National Instrutment Labview platform to gain satisfied real time performance with CompactRIO support. Robot detects environment using a 2D laser range finder with TCP/IP protocol. Line feature extraction process include area divided, iterative end point fit (IEPF) and a least square technique is introduced. Based on line feature, straight lines and corners as geometry features are obtained. The detected straight lines and corners are matched with the global straight lines and corners to obtain the robot position/orientation value. The odometry localization algorithm, straight line localization algorithm and corner localization algorithm are discussed. Different weight values are distributed to straight lines, corner and odometry according to the error model. As a result stable localization is achieved with position and orientation resolution as 50mm, 5 degree. A good performance for the method is also achieved with cycle time as 120ms. Experiment shows the effectiveness of the hybrid localization method.

Following an investigation undertaken by the publisher, we have determined that this paper was accepted on the basis of a compromised peer review process. We hereby retract the paper. The corresponding author has been notified of the retraction. The retraction statement can be found here: https://doi.org/10.1520/JTE20269995. 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.

Aiming at the complex and relatively closed underground environment of coal mine where mobile robot work, this paper proposes an autonomous localization method for coal mine underground mobile robot based on multi-sensor fusion. A simple robot localization model based on turning is established, which replaces the traditional method of estimating complex ground parameters in coal mine tunnel. In order to improve locating accuracy, a multi-sensor fusion localization algorithm based on EKF+AMCL (Extended Kalman Filter+ Adaptive Monte Carlo Localization) is proposed. The EKF algorithm is used to eliminate the white Gaussian noise in the path angle signal of the robot. By the AMCL algorithm, the location and path angle of the robot is fused with Lidar and Map data to obtain real time location and path angle after corrected errors. The experiment shows that in the established robot turning tunnel environment, the high-precision autonomous localization of the robot can be achieved, with an average location error of 0.027m and an average path angle error of 2.23°.

High-precision indoor localization and tracking are essential requirements for the safe navigation and task execution of autonomous mobile robots. Despite the growing importance of mobile robots in various areas, achieving precise indoor localization remains challenging due to signal interference, multipath propagation, and complex indoor layouts. In this work, we present the first comprehensive study comparing the accuracy of Bluetooth low energy (BLE), WiFi, and ultra wideband (UWB) technologies for the indoor localization of mobile robots under various circumstances. In the performed experiments, the error margin of the WiFi-based systems reached 608.7 cm, which is not tolerable for most applications. As a commonly used technology in the existing tracking systems, the accuracy of BLE-based systems is at least 44.12% better than that of WiFi-based systems. The error margin of the BLE-based system in tracking static and mobile robots was 191.7 cm and 340.1 cm, respectively. The experiments showed that even with a limited number of UWB anchors, the system provides acceptable accuracy for tracking the mobile robots. Using only four UWB beacons in an environment of about 431 m2 area, the maximum error margin of detected positions by the UWB-based tracking system remained below 13.1 cm and 28.9 cm on average for the static and mobile robots, respectively. This error margin is 88.05% lower than that of the BLE-based system and 93.27% lower than that of the WiFi-based system on average. The high tracking precision, the need for a lower number of anchors, and the decreasing hardware costs point out that UWB will be the dominating technology in indoor tracking systems in the near future.

For the issue of increased computational complexity to improve the positioning accuracy of robots leaving in the mobile robot localization method, this paper propose a new Monte Carlo localization algorithm. This method combinated the traditional particle filter algorithm with unscented Kalman filter, markovian Monte Carlo and reduced complexities level through dynamically updating the number of particles in particle collection and ensuring the accuracy of mobile robot localization. Simulation results show that the algorithm can not only inhibit the particle degradation and improve the positioning accuracy of the robot, but also in terms of computational complexity has increased significantly.

A key problem of mobile robots is that of the positioning capability in order to detect their locations in the operating environment. A promising technology used in indoor localization recently is Visible Light Communication (VLC). In this paper, an integrated Angle of Arrival-Received Signal Strength (AOA-RSS) localization method using the VLC, which could get high accuracy with simple hardware, is proposed. In addition, the Extended Kalman Filter (EKF) and Particle Filter (PF) algorithms are combined with the proposed AOA-RSS localization method in order to achieve higher accuracy of the mobile robot positioning. By computer simulation, the performance of the proposed AOA-RSS localization method combined with the EKF and PF algorithms are compared. The combination scheme outperforms the individual VLC localization with small error approximation of a few centimetres.

Precise robot localization is important for all mobile robots, which has to deal with accumulating odometry errors, onboard sensory noise, harsh environmental conditions, unstable or missing GPS signal, and absence or uncertainties of a global map. Yet, localization is considered in Smart Environments applying dynamic connectivity with external local sensors within the Internet of Things (IoT) paradigm. This paper presents experimental results of robot indoor localization using a single external smart RGB-D camera. The virtual experiments were performed in the ROS Gazebo simulator with Turtlebot3 Waffle Pi mobile robot model. Three types of robot motion within a virtual office environment were considered: static state, linear motion, and three different cases of curvilinear locomotion. In all cases, external RGB-D camera usage allowed to obtain a reasonably accurate location of the robot.