Wheel Odometer Research Articles

Robot SLAM Method Based on Multi-Sensor Fusion

This study presents an innovative multi-sensor fusion SLAM solution to address the significant error in particle proposal distribution, excessive particle resource consumption, and low algorithm execution efficiency encountered in traditional RBPF-SLAM algorithms in practical applications. The proposed solution aims to optimize the overall performance of the SLAM system by effectively integrating data from laser radar, inertial measurement unit (IMU), and wheel odometer, among other multi-source sensors.

Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter

Autonomous robots have gained significant attention in research due to their ability to facilitate human work. Navigation systems, particularly localization, present a challenge in autonomous robots. The inertial navigation system is a localization system that uses inertial sensors and a wheel odometer to estimate the robot’s relative position to the initial position. However, the system is susceptible to continuous error accumulation over time due to factors like sensor noise and wheel slip. To address these issues, external sensors are required to measure the robot’s position in the environment. The extended Kalman filter (EKF) method is utilized to estimate the robot’s position based on wheel odometer and light detection and ranging (LIDAR) sensor measurements. In the prediction stage, the input to the EKF is the position measurement from the wheel odometer, while the LIDAR sensor’s position measurement is used in the update stage to improve the prediction stage results. The test results reveal that the EKF’s estimated position has a lower average error compared to the position measurement using the wheel odometer. Therefore, it can be concluded that the EKF technique is effectively applied to the robot and can correct the wheel odometer's cumulative error with the assistance of the LIDAR sensor.

Indoor Positioning of AGVs Based on Multi-Sensor Data Fusion Such as LiDAR

In recent years, with the rapid growth in technology and demand for industrial robots, Automated Guided Vehicles (AGVs) have found widespread application in industrial workshops and smart logistics, emerging as a global hot research topic. Due to the volatile and complex working environments, the positioning technology of AGV robots is of paramount importance. To address the challenges associated with AGV robot positioning, such as significant accumulated errors in wheel odometer and Inertial Measurement Unit (IMU), susceptibility of Ultra Wide Band (UWB) positioning accuracy to Non Line of Sight (NLOS) errors, as well as the distortion points and drift in point clouds collected by LiDAR during robot motion, a novel positioning method is proposed. Initially, Weighted Extended Kalman Filter (W-EKF) is employed for the loosely coupled integration of wheel odometer and Ultra Wide Band (UWB) data, transformed into W-EKF pose factors. Subsequently, appropriate addition of W-EKF factors is made during the tight coupling of pre-integrated Inertial Measurement Unit (IMU) with 3D-LiDAR to counteract the distortion points, drift, and accumulated errors generated by LiDAR, thereby enhancing positioning accuracy. After experimentation, the algorithm achieved a final positioning error of only 6.9cm, representing an approximately 80% improvement in positioning accuracy compared to the loosely coupled integration of the two sensors.

Indoor Robot SLAM with Multi-Sensor Fusion

Abstract In order to solve the problem of large positioning error and incomplete mapping of SLAM based on two-dimensional lidar in indoor environment, a multi-sensor fusion SLAM algorithm for indoor robots was proposed. Aiming at the mismatch problem of the traditional ICP algorithm in the front end of the lidar SLAM, the algorithm adopts the PL-ICP algorithm that is more suitable for the indoor environment, and uses the extended Kalman filter to fuse the wheel odometer and IMU to provide the initial motion estimation value. Then, during the mapping phase, the pseudo 2D laser data converted from the 3D point cloud data obtained by the depth camera is fused with the data obtained from the 2D lidar to compensate for the lack of vertical field of view in the 2D lidar mapping. The final experimental results show that the fusion odometer data has improved the positioning accuracy by at least 33% compared to a single wheeled odometer, providing a higher initial iteration value for the PL-ICP algorithm. At the same time, fusion mapping compensates for the shortcomings of a single two-dimensional lidar mapping, and constructs an environmental map with more complete environmental information.

CID-SIMS: Complex indoor dataset with semantic information and multi-sensor data from a ground wheeled robot viewpoint

Simultaneous localization and mapping (SLAM) and 3D reconstruction have numerous applications for indoor ground wheeled robots such as floor sweeping and food delivery. To advance research in leveraging semantic information and multi-sensor data to enhance the performances of SLAM and 3D reconstruction in complex indoor scenes, we propose a novel and complex indoor dataset named CID-SIMS, where semantic annotated RGBD images, inertial measurement unit (IMU) measurements, and wheel odometer data are provided from a ground wheeled robot viewpoint. The dataset consists of 22 challenging sequences captured in nine different scenes including office building and apartment environments. Notably, our dataset achieves two significant breakthroughs. Firstly, semantic information and multi-sensor data are provided meanwhile for the first time. Secondly, GeoSLAM is utilized for the first time to generate ground truth trajectories and 3D point clouds within two-centimeter accuracy. With spatial-temporal synchronous ground truth trajectories and 3D point clouds, our dataset is capable of evaluating SLAM and 3D reconstruction algorithms in a unified global coordinate system. We evaluate state-of-the-art SLAM and 3D reconstruction approaches on our dataset, demonstrating that our benchmark is applicable. The dataset is publicly available on https://cid-sims.github.io.

Research on a system for the diagnosis and localization of conveyor belt deviations in belt conveyors

To address the shortcomings of existing conveyor belt deviation detection methods, such as the small detection range and slow detection speed, a method that utilizes machine vision technology to detect belt deviations in belt conveyors is proposed. A track-type inspection robot was used to collect real-time running images of a conveyor belt; Wiener filtering was carried out, grayscale processing was used, Canny edge detection, Hough transform and least squares fitting operations were used to extract the straight lines of the edges of the conveyor belt, and the offset from the straight lines of the conveyor belt under normal running conditions was calculated. To address the cumulative error problem caused by the long time and long distance in the process of locating the wheel odometer, a joint localization technology that used radio frequency identification (RFID) and a wheel odometer was proposed, and the error correction was carried out for a rotary encoder by using the RFID technology. The experimental results demonstrate that the detection method achieved a detection accuracy of over 92.1% with a detection speed of up to 31 FPS. This method enables the fast and precise identification of conveyor belt deviations, and it satisfies the requirements for real-time detection for belt conveyors. The location accuracy of the localization system that combined RFID and a wheel odometer was able to reach the centimeter level, and the system could accurately locate the positions of belt deviations. This is of vital significance for ensuring safety and efficiency in production by enterprises.

Design and Experiment of a Breakpoint Continuous Spraying System for Automatic-Guidance Boom Sprayers

Repeated and missed spraying are common problems during the working of boom sprayers, especially in the breakpoint continuous process. Therefore, the present study investigated a breakpoint continuous spraying system for automatic-guidance boom sprayers based on a hysteresis compensation algorithm for spraying. An operational breakpoint identification algorithm, which combines a real-time kinematic global navigation satellite system (RTK-GNSS) and wheel odometer, was proposed; a pre-adjusted proportional-integral-derivative (PID) control algorithm for the opening degree of the proportional control valve was designed in thus study. Tests were conducted to establish equations correlating the opening degree of the proportional control valve, pump output flow rate, and main pipeline flow rate, with an R2 ≥ 0.9525. The time to adjust to the target flow rate was experimentally tested. The breakpoint identification accuracy of the RTK-GNSS and RTK-GNSS + wheel odometer was experimentally assessed. A field spraying deposition variation experiment was conducted. According to the results, the system effectively eliminated missed spraying, with a maximum repeated spraying distance of ≤3.3 m, and it achieved a flow control error within 3%. This system also reduced the repeated spraying area and enhanced the pesticide spraying quality of breakpoint continuous spraying for automatic-guidance boom sprayers.

MEASUREMENT OF LINEAR VELOCITY USING A MOBILE ROBOTIC PLATFORM WITH COMPUTER VISION

This article presents an approach to integrating computer vision algorithms into the control system of traction electric drives in rail transport. It demonstrates the utilization of computer vision algorithms for calculating linear velocity as an alternative to conventional sensors like wheel odometers, GPS, DGPS and inertial sensors, which may prove ineffective on slippery surfaces and at low speeds. As a result, this article focuses on employing linear velocity as feedback within the control system to enhance power efficiency during starting and stopping and to prevent wheel slip. The electric drive control system has been successfully implemented and tested on a robotics platform designed for simulating dynamic behaviors in real rail transports scenarios. The article details the development process of this robotics platform, which is employed to mimic real-world dynamic conditions in rail transport. The proposed control algorithm for speed estimation is assessed using a specially designed test bench, revealing its capability to predict speed with a relatively high degree of accuracy. Additionally, an optical flow algorithm for velocity estimation is introduced and evaluated through a specially designed test rig, indicating a strong correlation between the predicted vehicle speed and the measurements from precision optical encoders. The study also determines the optimal feature window size for real-time optical flow rate estimation. In summary, this approach exhibits significant potential for accurate speed estimation. Ongoing experiments are being conducted under various real-world conditions, with future research aimed at developing a dependable autonomous system for speed measurement. The integration of modern digital computer vision technologies not only enhances the traction characteristics of electric drives but also extends the capabilities of traction electric drives to meet the rigorous demands of industrial equipment.

Multi-Sensor Fusion Tomato Picking Robot Localization and Mapping Research

To address the current problem of unsatisfactory localization and map building of an intelligent picking robot mobile platform in an unstructured picking environment, a multi-sensor fusion localization and map building method is proposed to fuse the visual odometer position estimation results with IMU and wheel odometer. And the pre-fusion processing of wheel odometer data is proposed to solve the problem of large errors of wheel odometers on uneven ground. The multi-sensor fusion scheme is proposed to fuse vision and laser SLAM methods to build a more information-rich point cloud and raster fusion map for the problem of non-closure caused by sparse branches. The experimental results show that the multi-sensor fusion SLAM method can obtain more accurate, richer, and more robust maps.

UWB-IMU-Odometer Fusion Localization Scheme: Observability Analysis and Experiments

In this article, we present a novel ultrawideband (UWB)-inertial measurement unit (IMU)-odometer fusion localization scheme for nonholonomic ground robots in global positioning system (GPS)-denied environments. To overcome the severe drift problems caused by the large acceleration bias of low-cost IMUs, rather than using the conventional IMU-only propagation model, a wheel odometer and a three-axis gyroscope are integrated to propagate the system states. Furthermore, the observability conditions of the proposed system with nonholonomic constraints are theoretically derived by a nonlinear observability analysis. The results reveal the minimum number of anchors (or leader robots) required for position observability (at least three anchors for UWB-time of arrival (ToA) measurements and one anchor for both ToA and angle-of-arrival (AOA) measurements). In addition, the system inputs (linear velocity and angular velocity along different axes) need to be excited for attitude observability. Simulations and experiments have verified that the proposed approach produces accurate position estimation and outperforms previous methods. Meanwhile, the position and attitude observability conditions have been verified through rich experiments, and the degenerated cases where the states cannot be observed are enumerated and tested, making the scheme complete.

Research on Indoor AGV Fusion Localization Based on Adaptive Weight EKF Using Multi-sensor

To solve the problem of large accumulative errors of wheel odometer and inertial measurement unit (IMU), which make pose calculation mistakes due to friction coefficient of the ground or bumps and collisions, furthermore, affect path planning and navigation of automated guided vehicle (AGV), an extensible fusion localization method based on multiple sensors is proposed. Based on the traditional extended Kalman filter (EKF), the internal positioning data is used for prediction, while the external positioning data is used for correction. According to the states of different sensors, the adaptive weight method is applied to eliminate the influence of system accumulative error and provide continuous and accurate positioning data. The experimental results show that compared to single-sensor, the fusion localization method has an increase in quantity, which can reach an accuracy within 5~8cm. It can deal with non-line-of-sight (NLOS) landmark occlusion situations, and can navigate in dim light or dark environments. The system ensures the accuracy of fusion data through visual positioning and ensures the stability of the system through the odometer and IMU. New positioning sensors are also possible to be added into the system for data fusion.

VWR-SLAM: Tightly Coupled SLAM System Based on Visible Light Positioning Landmark, Wheel Odometer, and RGB-D Camera

Visible light positioning (VLP) is a promising technology since it can provide high-accuracy indoor localization based on the existing lighting infrastructure. Most VLP systems require a prior light-emitting diode (LED) location map, termed a VLP-landmark map in this article, for which manual surveys are costly in practical deployment at scale. What is more, the existing approaches also require dense LED deployments. In this work, we proposed a multisensor fusion framework, termed VWR-simultaneous localization and mapping (SLAM), which tightly fused the VLP, wheel odometer, and red green dlue-depth map (RGB-D) camera to achieve SLAM. Our VWR-SLAM can provide accurate and robust robot localization and navigation in LED shortage/outage situations, meanwhile, constructing the 3-D sparse environment map and the 3-D VLP-landmark map without tedious manual measurements. The experimental results show that our proposed scheme can provide an average robot positioning accuracy of 1.81 cm and an LED mapping accuracy of 3.01 cm.

DESIGN OF A CONTROL SYSTEM FOR A SAFFLOWER PICKING ROBOT AND RESEARCH ON MULTISENSOR FUSION POSITIONING

This paper discusses the design of a safflower picking robot control system and focuses on a navigation control subsystem based on multisensor fusion. A navigation subsystem, an identification and positioning subsystem, a picking subsystem, and a levelling subsystem are designed. The hardware and software of the navigation subsystem are designed in detail, and a multisensor fusion positioning method based on extended Kalman fusion technology is proposed. The accuracy and stability levels of different combined navigation methods are compared. To test the effectiveness and accuracy of the proposed method, an outdoor test is carried out. The test results show that the outdoor fusion positioning accuracy of the robot is less than 8 cm, and when the satellite signal is lost, the navigation control subsystem can still provide high positioning accuracy. The final positioning result obtained using the integrated positioning method of the wheel odometer + IMU + DGNSS is approximately 52% higher than that of the odometer, approximately 29% higher than that of the wheel odometer + IMU, and approximately 11% higher than that of the IMU + DGNSS.

An enhanced positioning technique for underground pipeline robot based on inertial Sensor/Wheel odometer

For traditional underground pipeline robot based on the inertial sensor/wheel odometer, the outliers of the data collected by the positioning system, the attitude error of the inertial sensor and the velocity measurement error of the wheel odometer are crucial factors affecting positioning accuracy of the pipeline robot, and have a significant influence on underground pipeline maintenance and pipeline mapping. To solve the above three error factors, an enhanced positioning method for the underground pipeline robot based on the inertial sensor / wheel odometer is proposed in this paper. Firstly, basic principles including robot attitude calculation based on inertial sensors, robot velocity calculation based on wheel odometer, position calculation of pipeline robot and outliers elimination based on extrapolation fitting method are introduced; secondly, the velocity information and attitude angle of the pipeline robot is corrected by prior-backtracking method; thirdly, the round-trip positioning data optimization method based on adaptive weight is designed to improve the accuracy of the whole pipeline robot positioning system; finally, three pipelines with different lengths are introduced to verify the effectiveness and reliability of the proposed method. The experimental results show that the error of position information is no more than 0.18%, 0.07% and 0.11% of survey length, respectively.

Research on motion distortion optimization algorithm of laser SLAM

Aiming at the problems of inaccurate laser point cloud matching, large reference pose error and insufficient utilization of sensor observation data during 2D laser SLAM positioning and mapping, which lead to the motion distortion of lidar, a piecewise linear interpolation based method is proposed. Fusion of radar motion distortion optimization strategy and PL-ICP algorithm. The optimization algorithm first uses the piecewise linear interpolation method to fuse the data of the wheel odometer and the lidar, solve the robot pose corresponding to each laser point in the current frame of laser data, then convert all the laser points to the same coordinate system according to the solved pose, and finally the laser data scan matching is performed by the PL-ICP algorithm. In the experiment, the ICP and PL-ICP algorithms were compared for the radar data, and the SLAM algorithm equipped with the motion distortion optimization module was used for localization and mapping. The experimental results show that the motion distortion optimization algorithm effectively removes the motion distortion of low-precision lidar and improves the accuracy of mobile robot positioning and mapping.

Simultaneous Localization of Rail Vehicles and Mapping of Surroundings With LiDAR-Inertial-GNSS Integration

Accurate rail vehicle positioning is crucial for railroad operational safety. Modern light detection and ranging (LiDAR) simultaneously localization and mapping (SLAM) systems have delivered excellent results in real-world scenarios. However, it still lacks well investigation for rail vehicle applications. In this paper, we propose to achieve real-time accurate and robust positioning and mapping for rail vehicles utilizing LiDAR SLAM. Our framework tightly couples one non-repetitive scanning LiDAR with IMU, wheel odometer, and global navigation satellite system (GNSS) into pose estimation and simultaneous global map generation. As frontend, the IMU/odometer preintegration data de-skews the denoised point clouds and produces initial guess for LiDAR odometry. Besides, we leverage the plane constraints from extracted rail tracks and the height descriptor to further improve the system accuracy. As backend, a sliding window based factor graph is constructed to jointly optimize multi-modal information. To ensure a globally-consistent and less blurry mapping result, we develop a two-stage mapping method to register the submaps to the global. The proposed method is extensively evaluated on real-world datasets of numerous scenarios, including general-speed and high-speed ones, both freight traffic and passenger traffic is covered. The results show that our system delivers meter-level localization accuracy even in large or degenerated environments.

Visual-Inertial Odometry With Online Calibration of Velocity-Control Based Kinematic Motion Models

Visual-inertial odometry (VIO) is an important technology for autonomous robots with power and payload constraints. In this paper, we propose a novel approach for VIO with stereo cameras which integrates and calibrates the velocity-control based kinematic motion model of wheeled mobile robots online. Including such a motion model can help to improve the accuracy of VIO. Compared to several previous approaches proposed to integrate wheel odometer measurements for this purpose, our method does not require wheel encoders and can be applied when the robot motion can be modeled with velocity-control based kinematic motion model. We use radial basis function (RBF) kernels to compensate for the time delay and deviations between control commands and actual robot motion. The motion model is calibrated online by the VIO system and can be used as a forward model for motion control and planning. We evaluate our approach with data obtained in variously sized indoor environments, demonstrate improvements over a pure VIO method, and evaluate the prediction accuracy of the online calibrated model.

TOWARDS ROBUST INDOOR VISUAL SLAM AND DENSE RECONSTRUCTION FOR MOBILE ROBOTS

Abstract. Mobile robots are being increasingly employed in various indoor scenarios. The fundamental prerequisite is that the robot can reconstruct an accurate and complete map of the observed environment and estimate the track of its movements in this map. Current visual SLAM methods can perform this task reasonably well, but mostly in small spaces, such as a single room, and often tested in well-textured environments. In real-world applications of large indoor scenes, they lack robustness and fail to build a globally consistent map. To this end, we propose a novel system that can robustly solve the problem encountered by existing visual SLAM methods, such as weak texture and long-term drift. By combining information from a wheel odometer, the robot poses can be predicted smoothly in the absence of texture. The geometric cues are leveraged by aligning Truncated Signed Distance Function (TSDF) based submaps to minimize the long-term drift. To reconstruct a more complete and accurate dense map, we refine the sensor depth maps by taking advantage of color information and the optimization result of global bundle adjustment. As a result, the system can provide precise trajectory estimation and a globally consistent map for the downstream tasks. We validate the accuracy and robustness of the proposed method on both public and self-collected datasets and show the complementary nature of each module. Evaluation results based on high precision ground-truth show an improvement in the mean Absolute Trajectory Error (ATE) from 21 cm to 2 cm for the trajectory estimation, and the reconstructed map has a mean accuracy of 8 cm.

A Robust 2D Lidar SLAM Method in Complex Environment

The two-dimensional (2D) lidar is a ranging optical sensor that can measure the cross-section of the geometric structure of the environment. We propose a robust 2D lidar simultaneous localization and mapping (SLAM) algorithm working in ambiguous environments. To improve the front-end scan-matching module’s accuracy and robustness, we propose performing degeneration analysis, line landmark tracking, and environment coverage analysis. The max-clique selection and odometer verification are introduced to increase the stability of the SLAM algorithm in an ambiguous environment. Moreover, we propose a tightly coupled framework that integrates lidar, wheel odometer, and inertial measurement unit (IMU). The framework achieves the accurate mapping in large-scale environments using a factor graph to model the multi-sensor fusion SLAM problem. The experimental results demonstrate that the proposed method achieves a highly accurate front-end scan-matching module with an error of 3.8% of the existing method. And it can run stably in ambiguous environments where the existing method will be failed. Moreover, it ccan successfully construct a map with an area of more than 250 000 square meters.

An Enhanced Hybrid Visual-Inertial Odometry System for Indoor Mobile Robot.

As mobile robots are being widely used, accurate localization of the robot counts for the system. Compared with position systems with a single sensor, multi-sensor fusion systems provide better performance and increase the accuracy and robustness. At present, camera and IMU (Inertial Measurement Unit) fusion positioning is extensively studied and many representative Visual–Inertial Odometry (VIO) systems have been produced. Multi-State Constraint Kalman Filter (MSCKF), one of the tightly coupled filtering methods, is characterized by high accuracy and low computational load among typical VIO methods. In the general framework, IMU information is not used after predicting the state and covariance propagation. In this article, we proposed a framework which introduce IMU pre-integration result into MSCKF framework as observation information to improve the system positioning accuracy. Additionally, the system uses the Helmert variance component estimation (HVCE) method to adjust the weight between feature points and pre-integration to further improve the positioning accuracy. Similarly, this article uses the wheel odometer information of the mobile robot to perform zero speed detection, zero-speed update, and pre-integration update to enhance the positioning accuracy of the system. Finally, after experiments carried out in Gazebo simulation environment, public dataset and real scenarios, it is proved that the proposed algorithm has better accuracy results while ensuring real-time performance than existing mainstream algorithms.