Robot Localization Research Articles

A modular agricultural robotic system (MARS) for precision farming: Concept and implementation

AbstractIncreasing global population, climate change, and shortage of labor pose significant challenges for meeting the global food and fiber demand, and agricultural robots offer a promising solution to these challenges. This paper presents a new robotic system architecture and the resulting modular agricultural robotic system (MARS) that is an autonomous, multi‐purpose, and affordable robotic platform for in‐field plant high throughput phenotyping and precision farming. There are five essential hardware modules (wheel module, connection module, robot controller, robot frame, and power module) and three optional hardware modules (actuation module, sensing module, and smart attachment). Various combinations of the hardware modules can create different robot configurations for specific agricultural tasks. The software was designed using the Robot Operating System (ROS) with three modules: control module, navigation module, and vision module. A robot localization method using dual Global Navigation Satellite System antennas was developed. Two line‐following algorithms were implemented as the local planner for the ROS navigation stack. Based on the MARS design concept, two MARS designs were implemented: a low‐cost, lightweight robotic system named MARS mini and a heavy‐duty robot named MARS X. The autonomous navigation of both MARS X and mini was evaluated at different traveling speeds and payload levels, confirming satisfactory performances. The MARS X was further tested for its performance and navigation accuracy in a crop field, achieving a high accuracy over a 537 m long path with only 15% of the path having an error larger than 0.05 m. The MARS mini and MARS X were shown to be useful for plant phenotyping in two field tests. The modular design makes the robots easily adaptable to different agricultural tasks and the low‐cost feature makes it affordable for researchers and growers.

Localization of Mobile Robot Aided for Large-Scale Construction Based on Optimized Artificial Landmark Map in Ongoing Scene

The effectiveness of mobile robot aided for architectural construction depends strongly on its accurate localization ability. Localization of mobile robot is increasingly important for the printing of buildings in the construction scene. Although many available studies on the localization have been conducted, only a few studies have addressed the more challenging problem of localization for mobile robot in large-scale ongoing and featureless scenes. To realize the accurate localization of mobile robot in designated stations, we build an artificial landmark map and propose a novel nonlinear optimization algorithm based on graphs to reduce the uncertainty of the whole map. Then, the performances of localization for mobile robot based on the original and optimized map are compared and evaluated. Finally, experimental results show that the average absolute localization errors that adopted the proposed algorithm is reduced by about 21% compared to that of the original map.

Challenges and Opportunities for Autonomous Micro-UAVs in Precision Agriculture

Mobile robots such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) are increasingly used for precision agriculture. While UGVs have larger payload capabilities and longer operation time, they are limited to 2-D space. This makes UAVs better suited for tasks that require fast coverage, harsh terrain traversal, and high altitude or multilevel operation. However, it remains a challenging task to develop a reliable yet fully autonomous UAV system that can actively extract actionable information in large-scale cluttered agricultural environments. Such a system will have to estimate its own poses, build a map of the environment, navigate through obstacles, and act to gather information with limited onboard computation and battery life. In this survey, we first review recent advances in UAV hardware and software, ranging from novel platforms and sensors to state-of-the-art autonomous navigation, object detection and segmentation, robot localization, and mapping algorithms related to agriculture. We then provide a list of challenges in each field and potential opportunities for the broader adoption of UAVs in precision agriculture.

Self-supervised learning of LiDAR odometry based on spherical projection

Recently, the learning-based LiDAR odometry has obtained robust estimation results in the field of mobile robot localization, but most of them are constructed based on the idea of supervised learning. In the network training stage, these supervised learning-based methods rely heavily on real pose labels, which is defective in practical applications. Different from these methods, a novel self-supervised LiDAR odometry, namely SSLO, is proposed in this article. The proposed SSLO only uses unlabeled point cloud data to train the three-view pose network to complete the robot localization task. Specifically, first, due to the sparseness and disorder of the original LiDAR point cloud, it is difficult to use deep convolutional neural networks for feature extraction. In this article, the spherical projection of the point cloud is used to convert the original point cloud into a regular vertex map. Then the vertex map obtained by the projection is used as the input of the neural network. Second, in the network training phase, SSLO uses multiple geometric losses for different situations of matching point clouds and introduces uncertainty weights when calculating the losses to reduce the interference of noise or moving objects in the scene. Last but not least, the proposed method is not only used in the simulation experiments based on the KITTI dataset and Apollo-SouthBay dataset but also applied to a real-world wheeled robot SLAM task. Extensive experimental results show that the proposed method has good performance in different environments.

An Improved Localization of Mobile Robotic System Based on AMCL Algorithm

In the Mobile Robotics domain, the ability of robots to locate themselves is one of the most important events. By locating, mobile robots can obtain information about the environment and continuously track their position and direction. Among localization algorithms, the Adaptive Monte Carlo Localization (AMCL) algorithm is applied most often in robot localization, a two-dimensional environment probabilistic localization system to improve the problems such as high computational complexity and hijacking of mobile robots that exist in the traditional MCL method. The proposed method is based on 2D laser information, range finder information, and AMCL to accomplish the localization task. Furthermore, an optimized AMCL algorithm is proposed to increase the accuracy of localization in terrain that is easy to fail to locate, have a chance to locate successfully when a localization error occurs, and apply the optimized AMCL to the mobile robot system. From the experimental results, we know that the improved AMCL algorithm can enhance the positioning accuracy of the robot effectively, which has better practicality than the original AMCL.

Relative localization of swarm robotics based on the polar method

This article proposed a simple and efficient method for relative localization of swarm robotics. Firstly, the relative localization module of the micro-robot is designed to be composed of a compass and several pairs of infrared transceiver sensors evenly arranged on the edge of the robot. Based on the existing relative localization method and combined with the relative localization module, an improved relative localization method for swarm robots named as polar method is proposed, which includes distance calculation, relative direction calculation, and coordinates conversion. After that, a set of localization rules is designed to apply this method to the swarm system, which mainly include establishment of reference coordinate system, localization of the adjacent layer of the seed robot, and localization between layers, so that the initial coordinates of all robots can be obtained directly with only one seed robot. Then we described the implementation process of dynamic localization of swarm robots based on the initial static localization in the edge detour motion mode. Finally, the feasibility and flexibility of this localization method are verified by experiments on the swarm robotic platform, and the localization method is applied to realize the shape formation of swarm robots.

Realization of indoor and outdoor localization and navigation for quadruped robots

The quadruped robot has the advantages of flexible movement and large load because of the bionic leg structure. In order to improve their intelligence and autonomy in the complex environment, we build a highly integrated sensing system with lidar and an inertial measurement unit (IMU) on a quadruped robot. After the robot completes the construction of the map, point clouds are aligned using Normal Distribution Transform(NDT) registration to realize the localization of the robot. In the experimental part, we quantitatively tested the accuracy of outdoor localization and also completed indoor-outdoor navigation experiments for the quadruped robot. The experimental results validate the effectiveness of the perception system.

Ground landmark-based localisation for mobile robots using a calibrated camera

Ground landmark-based localisation for mobile robots using a calibrated camera

Robust Pose Estimation via Hybrid Point and Twin Line Reprojection for RGB-D Vision Navigation

Pose estimation is a crucial technique to achieve robot localization using natural features for an RGB-D vision navigation system. However, it still suffers from the impoverishment of robustness and accuracy in complex workspaces. A robust real-time pose estimation approach using hybrid point and twin line reprojection is proposed to allow accurate localization for a vision-based robot under various challenging scenarios including low-texture, low-structure, occlusion, and viewpoint and illumination changes. Firstly, an improved adaptive threshold (IAT)-based oriented fast and rotated Brief (ORB) feature detection method is developed to extract sufficient IAT-ORB point features, which are matched by a multi-stage KNN-RTC-CS-RANSAC (KRCR) refinement feature matching strategy to generate reliable point matches. Then a robust EDLines line feature matching method is developed by integrating dynamic matching (DM) and median absolute deviation (MAD) to obtain fine line matches. Furthermore, the hybrid point reprojection errors are built from the fusion of 3D-2D and 3D-3D point pairs, and the twin line reprojection errors are constructed by integrating virtual right-eye lines generated by depth measurement, which both are incorporated into a novel unified errors optimization model to achieve optimal motion estimation by minimizing the reprojection errors of points and lines concurrently. Finally, the extensive experiments verify the robustness, accuracy, and real-time performance of the proposed approach on the public datasets and our real indoor scenarios. The quantitative results demonstrate that our proposed approach gains more accurate and stable localization than other state-of-the-art methods under challenging environments.

Ground landmark-based localisation for mobile robots using a calibrated camera

Ground landmark-based localisation for mobile robots using a calibrated camera

Bayesian In-Situ Calibration of Multiport Antennas for DoA Estimation: Theory and Measurements

The DoA of radio waves is used for many applications, e.g. the localization of autonomous robots and smart vehicles. Estimating the DoA is possible with a multiport antenna, e.g. an antenna array or a multi-mode antenna (MMA). In practice, DoA estimation performance decisively depends on accurate knowledge of the antenna response, which makes antenna calibration vital. As the antenna surroundings influence its response, it is necessary to measure the entire device with installed antenna to obtain the installed antenna response. Antenna calibration is often done in a dedicated measurement chamber, which can be inconvenient and costly, especially for larger devices. Thus, auto- and in-situ calibration methods aim at making antenna calibration in a measurement chamber redundant. However, existing auto- and in-situ calibration methods are restricted to certain antenna types and certain calibrations. In this paper, we propose a Bayesian in-situ calibration algorithm based on a maximum a posteriori (MAP) estimator, which is suitable for arbitrary multiport antennas. The algorithm uses received signals from a transmitter, noisy external DoA observations, takes multipath propagation into account and does not require synchronization. Furthermore, we take an estimation theoretic perspective and provide an in-depth theoretical discussion of in-situ antenna calibration in unknown propagation conditions based on Bayesian information and the Bayesian Cramér-Rao bound (BCRB). Extensive simulations show that the proposed algorithm operates close to the BCRB and the achieved DoA estimation performance asymptotically approaches the case of a perfectly known antenna response. Finally, we provide an experimental validation, where we calibrate the antenna on a robotic rover and evaluate the DoA estimation performance using measurement data. With the proposed in-situ antenna calibration algorithm, DoA estimation performance is considerably improved compared to using an antenna response obtained by simulation or in a measurement chamber.

An E2ED-based Approach to Custom Robot Navigation and Localization

An E2ED-based Approach to Custom Robot Navigation and Localization

Indoor Localization Uncertainty Control Based on Wireless Ranging for Robots Path Planning

The accurate localization of robots in indoor environments is a well-known measurement problem, which becomes particularly critical in crowded scenarios, where collision with obstacles, objects, and human operators is very high. In this article, the problem is solved through an optimal path planning strategy that keeps into account the ranging measurement uncertainty from a given set of wireless anchors (e.g., ultra wideband (UWB) transceivers) that are used to track the position of the robot in a known reference frame through multilateration. In particular, assuming to use an observability-based filter (ObF), namely, a position tracking estimator conceived to ensure the global observability of the robot state (namely, its planar position and orientation) with quantifiable uncertainty, a potential-based path planning strategy is applied to drive the robot toward the goals while minimizing localization uncertainty. Several simulation results using two alternative artificial potential functions confirm the correctness and optimal performances of the proposed approach, which is entirely driven by observability and measurement uncertainty concerns and, as such, it is quite different and novel in the context of robots’ path planning techniques.

Monocular Visual-Inertial Odometry for Agricultural Environments

The accuracy of autonomous robot localization using a monocular visual-inertial odometry system (VIO) is significantly reduced in an agricultural environment compared to an urban and indoor environment due to the unstructured scenes with unstable features, variation of light conditions, and rugged terrain. To address those challenges, we propose a monocular VIO system with modifications to the existing state-of-the-art monocular VIO system VINS-mono. Three modules of VINS-mono have improved in this work: vision processing front end, pose optimization, failure detection and recovery. In the vision processing front-end module, we proposed a keyframe selection algorithm base on vertical movement smoothness verification to prevent the rapid loss of feature tracks. In the pose optimization module, some feature points are removed using a depth-limited approach to improve the efficiency and accuracy of trajectory tracking. In the failure detection and recovery module, we propose a failure recovery method using the old trajectory poses, which can recover the trajectory tracking process interrupted by the failure and alleviate the orientation drift. Experiments on the Rosario dataset have shown that our system outperforms VINS-mono, and the absolute trajectory error in the agricultural environment can be at least reduced by 69% compared with the VINS-mono, which can effectively improve the localization accuracy of agriculture robots in the agricultural environment.

Realization of Convergent Binocular Vision Algorithm for Guiding Robot Localization

Realization of Convergent Binocular Vision Algorithm for Guiding Robot Localization

Sensor fusion-based approach for the field robot localization on Rovitis 4.0 vineyard robot

Sensor fusion-based approach for the field robot localization on Rovitis 4.0 vineyard robot

Distributed Matrix Weighted Fusion Gaussian Filter

This letter is concerned with the distributed fusion estimation problem for nonlinear systems. Generally, optimal distributed weighted fusion criterion can be given by the form of analytic solution under the case of linear systems. However, this classical fusion criterion is not applicable to nonlinear systems because the error cross-covariances among local filters cannot be obtained. In this case, the statistical linear regression (SLR) method is firstly proposed to approximate the error cross-covariances among local Gaussian filters. To further reduce the performance loss caused by linearization errors in SLR, a measurement-dependent maximum likelihood criterion is developed to adjust the error cross-covariance matrices. Finally, a mobile robot localization example is employed to show the effectiveness and advantages of the proposed methods.

New trends on computer vision applied to mobile robot localization

This work presents a systematic review of computer vision techniques for locating mobile robots. Its main objectives are to analyze the latest technology in use to locate mobile robots. In addition, this work violated the advances achieved so far, assesses the challenges to be overcome and provides an analysis of future prospects. There is a lot of research related to the location of mobile robots, but the number of review articles on the topic is small, which makes this work of remarkable value. The research covered the works published in Web of Science, Scopus, Science Direct and IEEEXplore until June 2021. Each work found proposes a vision-based localization technique. After being analyzed individually, it can be concluded that it is necessary to assess the level of accuracy of these techniques through a single standard, it is necessary to assess the cost-benefit of the computational cost and the need for more powerful computers or systems to perform the localization task. Finally, the main contributions of this work are the creation of a table summarizing the main methods of localization by computer vision, the statistical analysis performed on the keywords of the selected works and providing an overview of this line of research that can be a basis for future research.

Drift-Free Visual SLAM for Mobile Robot Localization by Integrating UWB Technology

Visual simultaneous localization and mapping (vSLAM or visual SLAM) is an important technique for mobile robot localization in the global positioning system denied (GPS-denied) environments. However, the positioning accuracy could become unbearable due to the lack of image feature points when the robot navigates in a spacious indoor space. The drifting errors accumulated over time are generally inevitable and need to be mitigated by more sophisticated loop-closure algorithms. In this paper, we propose a drift-free visual SLAM technique for mobile robot localization by integrating the ultra-wideband (UWB) positioning technology. The basic concept is to utilize the global constraint of the UWB positioning to reduce the locally accumulated errors of visual SLAM localization based on the extended Kalman filtering (EKF) framework. In our experimental results, various SLAM approaches are performed in the indoor scenes, and the evaluation and comparison have demonstrated the feasibility of the proposed localization technique. By the integration of UWB positioning, the overall drift error of the robot navigation is reduced for more than 50%.

Development of Adaptive Line Tracking Breakpoint Detection Algorithm for Room Sensing using LiDAR Sensor

This research focuses on the use of Light Detection and Ranging (LiDAR) sensors for robot localization. One of the most essential algorithms in LiDAR localization is the breakpoint detector algorithm which is used to determine the corner of the room. The previously developed breakpoint detection methods have weaknesses, such as the Adaptive Breakpoint Detector (ABD), could generate dynamic threshold values. The ABD results, on the other hand, still require Line Extraction to obtain the corner breakpoint. Line Extraction method, e.g. Iterative End Point Fit (IEPF), is used to categorize data, resulting in the generation of a line pattern as an interpretation of a wall. The computational method for obtaining the corner breakpoint becomes longer as the line is extracted. To address this issue, our algorithm proposes a new threshold area in the form of an ellipse with the threshold value parameter obtained from previously identified room size and sensor characteristics. As a result the corner breakpoint detection becomes more adaptive. The goal of this research is to create an Adaptive Line Tracking Breakpoint Detector (ALTBD) approach that will reduce the computing time required to detect corner breakpoints. Furthermore, the Line Extraction method required for corner breakpoint detection is modified in the ALTBD. To distinguish between the edge of the wall and the corner of the room, the boundary value is increased. The ALTBD method was tested in a simulation arena comprised of multiple rooms and halls. According to the results, the ALTBD computation time is faster in detecting corner breakpoints than the ABD IEPF method, also the accuracy for determining the position of the robot was improved.