“…The second (Method II) and third (Method III) methods were novel techniques introduced in this study, with Method III differing from Method II only in the inclusion of IMU prediction. Finally, we compared our approach to LI-Calib proposed by ZJU [ 24 ] and LI-Init proposed by HKU [ 25 ]. By comparing the results of these four calibration methods, we aim to determine the most effective approach for lidar–IMU calibration.…”
Section: Resultsmentioning
confidence: 99%
“…Despite its potential, this method may have higher data requirements in practice. LI-Init is a robust, real-time initialization method for lidar–inertial systems proposed by Hong Kong University (HKU) [ 25 ]. The proposed method calibrates the temporal offset and extrinsic parameter between lidars and IMUs.…”
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.
“…The second (Method II) and third (Method III) methods were novel techniques introduced in this study, with Method III differing from Method II only in the inclusion of IMU prediction. Finally, we compared our approach to LI-Calib proposed by ZJU [ 24 ] and LI-Init proposed by HKU [ 25 ]. By comparing the results of these four calibration methods, we aim to determine the most effective approach for lidar–IMU calibration.…”
Section: Resultsmentioning
confidence: 99%
“…Despite its potential, this method may have higher data requirements in practice. LI-Init is a robust, real-time initialization method for lidar–inertial systems proposed by Hong Kong University (HKU) [ 25 ]. The proposed method calibrates the temporal offset and extrinsic parameter between lidars and IMUs.…”
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.
“…The LiDAR coordinate system is denoted as (·) L , the IMU coordinate system is denoted as (·) I , and the first frame’s IMU position in the mapping process is selected as the global map coordinate system denoted as (·) G . The extrinsic calibration between LiDAR and IMU is determined to be fixed as TLI=(RLI,pLI) through a calibration method (Zhu et al , 2022). Ttrue^ represents the predicted pose from the Kalman filter, and Ttrue¯ represents the updated pose.…”
Section: Initial Mapping With Global Optimizationmentioning
Purpose
Autonomous mobile robots (AMRs) play a crucial role in industrial and service fields. The paper aims to build a LiDAR-based simultaneous localization and mapping (SLAM) system used by AMRs to overcome challenges in dynamic and changing environments.
Design/methodology/approach
This research introduces SLAM-RAMU, a lifelong SLAM system that addresses these challenges by providing precise and consistent relocalization and autonomous map updating (RAMU). During the mapping process, local odometry is obtained using iterative error state Kalman filtering, while back-end loop detection and global pose graph optimization are used for accurate trajectory correction. In addition, a fast point cloud segmentation module is incorporated to robustly distinguish between floor, walls and roof in the environment. The segmented point clouds are then used to generate a 2.5D grid map, with particular emphasis on floor detection to filter the prior map and eliminate dynamic artifacts. In the positioning process, an initial pose alignment method is designed, which combines 2D branch-and-bound search with 3D iterative closest point registration. This method ensures high accuracy even in scenes with similar characteristics. Subsequently, scan-to-map registration is performed using the segmented point cloud on the prior map. The system also includes a map updating module that takes into account historical point cloud segmentation results. It selectively incorporates or excludes new point cloud data to ensure consistent reflection of the real environment in the map.
Findings
The performance of the SLAM-RAMU system was evaluated in real-world environments and compared against state-of-the-art (SOTA) methods. The results demonstrate that SLAM-RAMU achieves higher mapping quality and relocalization accuracy and exhibits robustness against dynamic obstacles and environmental changes.
Originality/value
Compared to other SOTA methods in simulation and real environments, SLAM-RAMU showed higher mapping quality, faster initial aligning speed and higher repeated localization accuracy.
“…Consequently, the accuracy of calculating LiDAR odometry through frame-to-frame matching is compromised and the initial value estimation will be more difficult, making it challenging to obtain precise extrinsic parameters in the Xsens coordinate system through direct calibration using existing technology. Although we have explored various good open-source calibration methods for directly calibrating the VLP-32C and Xsens, such as LiDAR_-IMU_Init Zhu et al (2022), the results have proven unsatisfactory, as depicted in Figure 17.…”
Section: The Translation Vector T Imumentioning
confidence: 99%
“…Consequently, the accuracy of calculating LiDAR odometry through frame-to-frame matching is compromised and the initial value estimation will be more difficult, making it challenging to obtain precise extrinsic parameters in the Xsens coordinate system through direct calibration using existing technology. Although we have explored various good open-source calibration methods for directly calibrating the VLP-32C and Xsens, such as LiDAR_IMU_Init Zhu et al (2022), the results have proven unsatisfactory, as depicted in Figure 17.Figure 17.The failure scenario of direct calibration between Velodyne VLP-32C and Xsens MTi-G-710, when the sensor platform is in a slow movement during initialization, occurring significant drift in the calibration algorithm. LiDAR_IMU_Init Zhu et al is used.…”
Section: Sensor Synchronization and Calibrationmentioning
In this paper, we present the USTC FLICAR Dataset, which is dedicated to the development of simultaneous localization and mapping and precise 3D reconstruction of the workspace for heavy-duty autonomous aerial work robots. In recent years, numerous public datasets have played significant roles in the advancement of autonomous cars and unmanned aerial vehicles (UAVs). However, these two platforms differ from aerial work robots: UAVs are limited in their payload capacity, while cars are restricted to two-dimensional movements. To fill this gap, we create the “Giraffe” mapping robot based on a bucket truck, which is equipped with a variety of well-calibrated and synchronized sensors: four 3D LiDARs, two stereo cameras, two monocular cameras, Inertial Measurement Units (IMUs), and a GNSS/INS system. A laser tracker is used to record the millimeter-level ground truth positions. We also make its ground twin, the “Okapi” mapping robot, to gather data for comparison. The proposed dataset extends the typical autonomous driving sensing suite to aerial scenes, demonstrating the potential of combining autonomous driving perception systems with bucket trucks to create a versatile autonomous aerial working platform. Moreover, based on the Segment Anything Model (SAM), we produce the Semantic FLICAR dataset, which provides fine-grained semantic segmentation annotations for multimodal continuous data in both temporal and spatial dimensions. The dataset is available for download at: https://ustc-flicar.github.io/ .
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