Search Results (17)

To address the shortcomings of light detection and ranging (LiDAR) sensors in extracting road surface elevation information in front of a vehicle, a scheme for digital terrain construction based on the fusion of an Inertial Measurement Unit (IMU) and LiDAR perception is proposed. First, two sets of sensor coordinate systems were configured, and the parameters of LiDAR and IMU were calibrated. Then, a terrain construction system based on the fusion perception of IMU and LiDAR was established, and improvements were made to the state estimation and mapping architecture. Terrain construction experiments were conducted in an academic setting. Finally, based on the output information from the terrain construction system, a moving average-like algorithm was designed to process point cloud data and extract the road surface elevation information at the vehicle’s trajectory position. By comparing the extraction effects of four different sliding window widths, the 4 cm width sliding window, which yielded the best results, was ultimately selected, making the extracted road surface elevation information more accurate and effective. Full article

Handheld LiDAR scanners, which typically consist of a LiDAR sensor, Inertial Measurement Unit, and processor, enable data capture while moving, offering flexibility for various applications, including indoor and outdoor 3D mapping in fields such as architecture and civil engineering. Unlike fixed LiDAR systems, handheld devices allow data collection from different angles, but this mobility introduces challenges in data quality, particularly when initial calibration between sensors is not precise. Accurate LiDAR-IMU calibration, essential for mapping accuracy in Simultaneous Localization and Mapping applications, involves precise alignment of the sensors’ extrinsic parameters. This research presents a robust initial pose calibration method for LiDAR-IMU systems in handheld devices, specifically designed for indoor environments. The research contributions are twofold. Firstly, we present a robust plane detection method for LiDAR data. This plane detection method removes the noise caused by mobility of scanning device and provides accurate planes for precise LiDAR initial pose estimation. Secondly, we present a robust planes-aided LiDAR calibration method that estimates the initial pose. By employing this LiDAR calibration method, an efficient LiDAR-IMU calibration is achieved for accurate mapping. Experimental results demonstrate that the proposed method achieves lower calibration errors and improved computational efficiency compared to existing methods. Full article

Accurate positioning at the inter-row canopy can provide data support for precision variable-rate spraying. Therefore, there is an urgent need to design a reliable positioning method for the inter-row canopy of closed orchards (planted forests). In the study, the Extended Kalman Filter (EKF) fusion positioning method (method C) was first constructed by calibrating the IMU and encoder with errors. Meanwhile, 3D Light Detection and Ranging (LiDAR) observations were introduced to be fused into Method C. An EKF fusion positioning method (method D) based on 3D LiDAR corrected detection was designed. The method starts or closes method C by the presence or absence of the canopy. The vertically installed 3D LiDAR detected the canopy body center, providing the vehicle with inter-row vertical distance and heading. They were obtained through the distance between the center of the body and fixed row spacing. This can provide an accurate initial position for method C and correct the positioning trajectory. Finally, the positioning and canopy length measurement experiments were designed using a GPS positioning system. The results show that the method proposed in this study can significantly improve the accuracy of length measurement and positioning at the inter-row canopy, which does not significantly change with the distance traveled. In the orchard experiment, the average positioning deviations of the lateral and vertical distances at the inter-row canopy are 0.1 m and 0.2 m, respectively, with an average heading deviation of 6.75°, and the average relative error of canopy length measurement was 4.35%. The method can provide a simple and reliable inter-row positioning method for current remote-controlled and manned agricultural machinery when working in standardized 3D crops. This can modify the above-mentioned machinery to improve its automation level. Full article

This research proposes a lightweight and applicable dataset with a precise elevation ground truth and extrinsic calibration toward the LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and Mapping) task in the field of autonomous driving. Our dataset focuses on more cost-effective platforms with limited computational power and low-resolution three-dimensional LiDAR sensors (16-beam LiDAR), and fills the gaps in the existing literature. Our data include abundant scenarios that include degenerated environments, dynamic objects, and large slope terrain to facilitate the investigation of the performance of the SLAM system. We provided the ground truth pose from RTK-GPS and carefully rectified its elevation errors, and designed an extra method to evaluate the vertical drift. The module for calibrating the LiDAR and IMU was also enhanced to ensure the precision of point cloud data. The reliability and applicability of the dataset are fully tested through a series of experiments using several state-of-the-art LiDAR SLAM methods. Full article

Unmanned aerial vehicles (UAVs), also known as drones, are a cost-effective alternative to traditional surveying methods, and they can be used to collect geospatial data over inaccessible or hard-to-reach locations. UAV-integrated miniaturized remote sensing sensors such as hyperspectral and LiDAR sensors, which formerly operated on airborne and spaceborne platforms, have recently been developed. Their accuracies can still be guaranteed when incorporating pieces of equipment such as ground control points (GCPs) and field spectrometers. This study conducted three experiments for geometric and radiometric accuracy assessments of simultaneously acquired RGB, hyperspectral, and LiDAR data from a single mission. Our RGB and hyperspectral data generated orthorectified images based on direct georeferencing without any GCPs. Because of this, a base station is required for the post-processed Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) data. First, we compared the geometric accuracy of orthorectified RGB and hyperspectral images relative to the distance of the base station to determine which base station should be used. Second, point clouds could be generated from overlapped RGB images and a LiDAR sensor. We quantitatively and qualitatively compared RGB and LiDAR point clouds in this experiment. Lastly, we evaluated the radiometric quality of hyperspectral images, which is the most critical factor of the hyperspectral sensor, using reference spectra that was simultaneously measured by a field spectrometer. Consequently, the distance of the base station for post-processing the GNSS/IMU data was found to have no significant impact on the geometric accuracy, indicating that a dedicated base station is not always necessary. Our experimental results demonstrated geometric errors of less than two hyperspectral pixels without using GCPs, achieving a level of accuracy that is comparable to survey-level standards. Regarding the comparison of RGB- and LiDAR-based point clouds, RGB point clouds exhibited noise and lacked details; however, through the cleaning process, their vertical accuracy was found to be comparable with LiDAR’s accuracy. Although photogrammetry generated denser point clouds compared with LiDAR, the overall quality for extracting the elevation data greatly relies on factors such as the original image quality, including the image’s occlusions, shadows, and tie-points, for matching. Furthermore, the image spectra derived from hyperspectral data consistently demonstrated high radiometric quality without the need for in situ field spectrum information. This finding indicates that in situ field spectra are not always required to guarantee the radiometric quality of hyperspectral data, as long as well-calibrated targets are utilized. Full article

Calibration of sensors is critical for the precise functioning of lidar–IMU systems. However, the accuracy of the system can be compromised if motion distortion is not considered. This study proposes a novel uncontrolled two-step iterative calibration algorithm that eliminates motion distortion and improves the accuracy of lidar–IMU systems. Initially, the algorithm corrects the distortion of rotational motion by matching the original inter-frame point cloud. Then, the point cloud is further matched with IMU after the prediction of attitude. The algorithm performs iterative motion distortion correction and rotation matrix calculation to obtain high-precision calibration results. In comparison with existing algorithms, the proposed algorithm boasts high accuracy, robustness, and efficiency. This high-precision calibration result can benefit a wide range of acquisition platforms, including handheld, unmanned ground vehicle (UGV), and backpack lidar–IMU systems. Full article

Multi-line LiDAR and GPS/IMU are widely used in autonomous driving and robotics, such as simultaneous localization and mapping (SLAM). Calibrating the extrinsic parameters of each sensor is a necessary condition for multi-sensor fusion. The calibration of each sensor directly affects the accurate positioning control and perception performance of the vehicle. Through the algorithm, accurate extrinsic parameters and a symmetric covariance matrix of extrinsic parameters can be obtained as a measure of the confidence of the extrinsic parameters. As for the calibration of LiDAR-GPS/IMU, many calibration methods require specific vehicle motion or manual calibration marking scenes to ensure good constraint of the problem, resulting in high costs and a low degree of automation. To solve this problem, we propose a new two-step self-calibration method, which includes extrinsic parameter initialization and refinement. The initialization part decouples the extrinsic parameters from the rotation and translation part, first calculating the reliable initial rotation through the rotation constraints, then calculating the initial translation after obtaining a reliable initial rotation, and eliminating the accumulated drift of LiDAR odometry by loop closure to complete the map construction. In the refinement part, the LiDAR odometry is obtained through scan-to-map registration and is tightly coupled with the IMU. The constraints of the absolute pose in the map refined the extrinsic parameters. Our method is validated in the simulation and real environments, and the results show that the proposed method has high accuracy and robustness. Full article

Light detection and ranging (LiDAR) is often combined with an inertial measurement unit (IMU) to get the LiDAR inertial odometry (LIO) for robot localization and mapping. In order to apply LIO efficiently and non-specialistically, self-calibration LIO is a hot research topic in the related community. Spinning LiDAR (SLiDAR), which uses an additional rotating mechanism to spin a common LiDAR and scan the surrounding environment, achieves a large field of view (FoV) with low cost. Unlike common LiDAR, in addition to the calibration between the IMU and the LiDAR, the self-calibration odometer for SLiDAR must also consider the mechanism calibration between the rotating mechanism and the LiDAR. However, existing self-calibration LIO methods require the LiDAR to be rigidly attached to the IMU and do not take the mechanism calibration into account, which cannot be applied to the SLiDAR. In this paper, we propose firstly a novel self-calibration odometry scheme for SLiDAR, named the online multiple calibration inertial odometer (OMC-SLIO) method, which allows online estimation of multiple extrinsic parameters among the LiDAR, rotating mechanism and IMU, as well as the odometer state. Specially, considering that the rotating and static parts of the motor encoder inside the SLiDAR are rigidly connected to the LiDAR and IMU respectively, we formulate the calibration within the SLiDAR as two separate sets of calibrations: the mechanism calibration between the LiDAR and the rotating part of the motor encoder and the sensor calibration between the static part of the motor encoder and the IMU. Based on such a SLiDAR calibration formulation, we can construct a well-defined kinematic model from the LiDAR to the IMU with the angular information from the motor encoder. Based on the kinematic model, a two-stage motion compensation method is presented to eliminate the point cloud distortion resulting from LiDAR spinning and platform motion. Furthermore, the mechanism and sensor calibration as well as the odometer state are wrapped in a measurement model and estimated via an error-state iterative extended Kalman filter (ESIEKF). Experimental results show that our OMC-SLIO is effective and attains excellent performance. Full article

The fusion of light detection and ranging (LiDAR) and inertial measurement unit (IMU) sensing information can effectively improve the environment modeling and localization accuracy of navigation systems. To realize the spatiotemporal unification of data collected by the IMU and the LiDAR, a two-step spatiotemporal calibration method combining coarse and fine is proposed. The method mainly includes two aspects: (1) Modeling continuous-time trajectories of IMU attitude motion using B-spline basis functions; the motion of the LiDAR is estimated by using the normal distributions transform (NDT) point cloud registration algorithm, taking the Hausdorff distance between the local trajectories as the cost function and combining it with the hand–eye calibration method to solve the initial value of the spatiotemporal relationship between the two sensors’ coordinate systems, and then using the measurement data of the IMU to correct the LiDAR distortion. (2) According to the IMU preintegration, and the point, line, and plane features of the lidar point cloud, the corresponding nonlinear optimization objective function is constructed. Combined with the corrected LiDAR data and the initial value of the spatiotemporal calibration of the coordinate systems, the target is optimized under the nonlinear graph optimization framework. The rationality, accuracy, and robustness of the proposed algorithm are verified by simulation analysis and actual test experiments. The results show that the accuracy of the proposed algorithm in the spatial coordinate system relationship calibration was better than 0.08° (3δ) and 5 mm (3δ), respectively, and the time deviation calibration accuracy was better than 0.1 ms and had strong environmental adaptability. This can meet the high-precision calibration requirements of multisensor spatiotemporal parameters of field robot navigation systems. Full article

To improve the motion distortion caused by LiDAR data at low and medium frame rates when moving, this paper proposes an improved algorithm for scanning matching of estimated velocity that combines an IMU and odometer. First, the information of the IMU and the odometer is fused, and the pose of the LiDAR is obtained using the linear interpolation method. The ICP method is used to scan and match the LiDAR data. The data fused by the IMU and the odometer provide the optimal initial value for the ICP. The estimated speed of the LiDAR is introduced as the termination condition of the ICP method iteration to realize the compensation of the LiDAR data. The experimental comparative analysis shows that the algorithm is better than the ICP algorithm and the VICP algorithm in matching accuracy. Full article

This paper proposes a human gait tracking system using a dual foot-mounted IMU and multiple 2D LiDARs. The combining system aims to overcome the disadvantages of each single sensor system (the short tracking range of the single 2D LiDAR and the drift errors of the IMU system). The LiDARs act as anchors to mitigate the errors of an inertial navigation algorithm. In our system, two 2D LiDARs are used. LiDAR 1 is placed around the starting point, and LiDAR 2 is placed at the ending point (in straight walking) or at the turning point (in rectangular path walking). Using the LiDAR 1, we can estimate the initial headings and positions of each IMU without any calibration process. We also propose a method to calibrate two LiDARs that are placed far apart. Then, the measurement from two LiDARs can be combined in a Kalman filter and the smoother algorithm to correct the two estimated feet trajectories. If straight walking is detected, we update the current stride heading and the foot position using the previous stride headings. Then, it is used as a measurement update in the Kalman filter. In the smoother algorithm, a step width constraint is used as a measurement update. We evaluate the stride length estimation through a straight walking experiment along a corridor. The root mean square errors compared with an optical tracking system are less than 3 cm. The performance of proposed method is also verified with a rectangular path walking experiment. Full article

Light detection and ranging (LiDAR) is one of the popular technologies to acquire critical information for building information modelling. To allow an automatic acquirement of building information, the first and most important step of LiDAR technology is to accurately determine the important gesture information that micro electromechanical (MEMS) based inertial measurement unit (IMU) sensors can provide from the moving robot. However, during the practical building mapping, serious errors may happen due to the inappropriate installation of a MEMS-IMU. Through this study, we analyzed the different systematic errors, such as biases, scale errors, and axial installation deviation, that happened during the building mapping, based on a robot equipped with MEMS-IMU. Based on this, an error calibration model was developed. The problems of the deviation between the calibrated and horizontal planes were solved by a new sampling method. For this method, the calibrated plane was rotated twice; the gravity acceleration of the six sides of the MEMS-IMU was also calibrated by the practical values, and the whole calibration process was completed after solving developed model based on the least-squares method. Finally, the building mapping was then calibrated based on the error calibration model, and also the Gmapping algorithm. It was indicated from the experiments that the proposed model is useful for the error calibration, which can increase the prediction accuracy of yaw by 1–2° based on MEMS-IMU; the mapping results are more accurate when compared to the previous methods. The research outcomes can provide a practical basis for the construction of the building information modelling model. Full article

This paper presents a human-carried mapping backpack based on a pair of Velodyne LiDAR scanners. Our system is a universal solution for both large scale outdoor and smaller indoor environments. It benefits from a combination of two LiDAR scanners, which makes the odometry estimation more precise. The scanners are mounted under different angles, thus a larger space around the backpack is scanned. By fusion with GNSS/INS sub-system, the mapping of featureless environments and the georeferencing of resulting point cloud is possible. By deploying SoA methods for registration and the loop closure optimization, it provides sufficient precision for many applications in BIM (Building Information Modeling), inventory check, construction planning, etc. In our indoor experiments, we evaluated our proposed backpack against ZEB-1 solution, using FARO terrestrial scanner as the reference, yielding similar results in terms of precision, while our system provides higher data density, laser intensity readings, and scalability for large environments. Full article

This paper details a new automatic calibration method for the boresight angles between a LiDAR (Light Detection and Ranging) and an inertial measurement unit (IMU), based on a data selection algorithm, followed by the adjustment of boresight angles. This method, called LiDAR-IMU boresight automatic calibration (LIBAC), takes in input overlapping survey strips following simple line patterns over regular slopes. We first construct a boresight error observability criterion, used to select automatically the most sensitive points to boresight errors. From these points, we adjust the boresight angles. From a statistical analysis of the adjustment results, we derive the boresight angle precision. Results obtained with LIBAC on several LiDAR system integrated within drones are presented. We also give results about the reproducibility of the method. Full article

Mobile LiDAR Scanning (MLS) systems and UAV LiDAR Scanning (ULS) systems equipped with precise Global Navigation Satellite System (GNSS)/Inertial Measurement Unit (IMU) positioning units and LiDAR sensors are used at an increasing rate for the acquisition of high density and high accuracy point clouds because of their safety and efficiency. Without careful calibration of the boresight angles of the MLS systems and ULS systems, the accuracy of data acquired would degrade severely. This paper proposes an automatic boresight self-calibration method for the MLS systems and ULS systems using acquired multi-strip point clouds. The boresight angles of MLS systems and ULS systems are expressed in the direct geo-referencing equation and corrected by minimizing the misalignments between points scanned from different directions and different strips. Two datasets scanned by MLS systems and two datasets scanned by ULS systems were used to verify the proposed boresight calibration method. The experimental results show that the root mean square errors (RMSE) of misalignments between point correspondences of the four datasets after boresight calibration are 2.1 cm, 3.4 cm, 5.4 cm, and 6.1 cm, respectively, which are reduced by 59.6%, 75.4%, 78.0%, and 94.8% compared with those before boresight calibration. Full article