Mobile robot localization based on omnicamera

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.

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).

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

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.

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.

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.

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.

Recently, Simultaneous Localization and Mapping (SLAM) has achieved good performance in a certain environment. However, in a complex indoor environment, the tracking of the position of the mobile robot in Visual SLAM may get lost in some scenes with hardly any features. This paper presents a mobile robot localization method using an asynchronous data fusion formed by a wheel odometer and a visual odometer in the Extended Kalman Filter (EKF) based on the Semantic SLAM system. The first approach uses Semantic SLAM based on ORB-SLAM2 with YOLOv3, to get rid of the dynamic object while localizing and mapping, and build a global point cloud map where keyframes information is stored. Then, wheel odometer information and visual odometer information acquire after using a global point cloud map. Finally, the asynchronous data fusion for mobile robot localization, composed of visual odometer and wheel odometer, is realized at a frequency of about 50Hz in EKF. The experimental results on the real robot validate the method in accuracy and robustness by comparing it with ground truth from the motion capture measurement system in indoor environment.

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.

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.

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.

Mobile robot localization and map building are very important for a mobile robot to perform a navigation-based service task to aid the aged or disabled in an indoor environment (office, facilities or at home). In order to enable mobile robot to perform service tasks autonomously, the information of environment in which the mobile robot moves is needed. To obtain the accurate map, many different sensors system and techniques have been developed. Approaches of mapping can roughly be classified according to the kind of sensor data processed and the matching algorithms [S. Thrun et al., 1996, 1998, 2000, 2004; Chong, K.S et al., 1996; C.-C. Wang et al., 2002; G. Dissanayake et al., 2000; B. barshan et al., 1995; Weimin Shen et al., 2005]. In this paper, we proposed a method of map building using interactive GUI for a mobile robot. The reason we proposed this method is that it is difficult for a mobile robot to generate an accurate map although many kinds of sensors are used. The proposed method enables the operator to modify map built by sensors, compared with the real-time video from web camera by using modification tool in interactive GUI. Laser Range Finder (LRF) was used to get the information of the environment in which mobile robot moving. In order to improve self-localization of mobile robot, Extended Kalman Filter (EKF) was introduced. By the results of simulation and experiments, the developed EKF is effective in self-localization of mobile robot. This paper introduces the architecture of the system and gives some experimental results to verify the effectiveness of the developed system. The rest of the paper consists of 5 sections. Section 2 describes the structure of the proposed system. Section 3 presents the developed algorithm of self-localization of mobile robot and gives some simulation and experimental results to verify the effectiveness of the EKF in improving precision of positioning of mobile robot. Section 4 details the developed LRF data processing algorithm. Section 5 introduces the developed interactive GUI. The experimental results are given in Section 6. Section 7 concludes the paper. 13

Methods for mobile robot localization that use eigenspaces of panoramic snapshots of the environment are in general sensitive to changes in the illumination of the environment. Therefore, we propose an approach which achieves a reliable localization under severe illumination conditions. The method uses gradient filtering of the eigenspace. After testing the approach on images obtained by a mobile robot, we show that it outperforms the standard eigenspace-based recognition method.

The architecture of a Gaussian mixture Bayes (GMB) robot position estimation system

We propose a novel algorithm for improved localization of the mobile robots by fusing RFID localization system and ultra-sonic sensors. Because there are uncertainties in previous RFID localization system, we are focusing on the sensor fusion system, which removes the uncertainty of the robot location using the distance measurements by ultra-sonic sensors. We divide into 2 parts, which are GPE (global position estimation) by RFID localization system and LEC (local environment cognition) by ultra-sonic sensor. The scheme to remove the uncertainty is proposed, which are the modeling about the measurement noise for RFID localization system and to estimate the straight obstacle and circular obstacle in well-structural indoor environment by the ultra-sonic sensors. The hierarchical localization algorithm is proposed to estimate the position of the mobile robot from GPE and LEC and for robust localization under the unknown indoor environment.