A global and feature-based hybrid algorithm, which integrates global information and feature-based simultaneous localization and mapping (GF-SLAM). This system can operate adaptively when external signals are unstable, thereby avoiding cumulative errors produced by local methods. In agricultural planting bases with abundant circular arc features, the focus is on efficiently exploring the correlations between these features to optimize the robust real-time positioning system for work vehicles. In this process, feature-based SLAM (F-SLAM) is applied to partial positioning using a particle filter. Available global information is then fused using an extended Kalman filter (EKF) for precise positioning and deviation correction in the mapping process, thereby achieving an effective combination of two positioning modes. The proposed model was evaluated using two simulation environments and a comparison with representative techniques. Results showed that GF-SLAM was competitive in normal conditions while requiring fewer computations and significantly reducing the drift in F-SLAM for stable global signals. Switching between these two algorithms eliminated positioning errors to within 1 cm for a test case in which global localization was lost in 54.7% of the route, producing an error within 3 cm. The code will be open source. Note to Practitioners—Our adaptive fusion strategy aims to address challenges in real-world agricultural scenarios where global information may be lacking or unstable. This approach enhances the robustness and reliability of robot positioning in practical applications. We invite practitioners to consider the adaptability of our system to diverse environments, particularly those with limited or fluctuating global information. In this paper, we first introduce the EKF module for global localization, then illustrate the establishment of feature maps and particle filter positioning in F-SLAM. We conduct comparison experiments in two virtual environments and real agricultural planting bases, where approximately half of the route lacks global information. The results demonstrate the benefits of our adaptive fusion strategy in practical positioning applications. We also plan to explore the integration of additional sensors, such as cameras, combined with deep learning, to further improve the efficiency and quality of feature extraction. This extension is aimed at mitigating the issue of positioning failure caused by crop and equipment occlusion in agricultural scenarios.

Cooperative LiDAR systems integrating vehicles and road infrastructure, termed V2I calibration, exhibit substantial potential, yet their deployment encounters numerous challenges. A pivotal aspect of ensuring data accuracy and consistency across such systems involves the calibration of LiDAR units across heterogeneous vehicular and infrastructural endpoints. This necessitates the development of calibration methods that are both real-time and robust, particularly those that can ensure robust performance in urban canyon scenarios without relying on initial positioning values. Accordingly, this paper introduces a novel approach to V2I calibration, leveraging spatial association information among perceived objects. Central to this method is the innovative Overall Intersection over Union (oIoU) metric, which quantifies the correlation between targets identified by vehicle and infrastructure systems, thereby facilitating the real-time monitoring of calibration results. Our approach involves identifying common targets within the perception results of vehicle and infrastructure LiDAR systems through the construction of an affinity matrix. These common targets then form the basis for the calculation and optimization of extrinsic parameters. Comparative and ablation studies conducted using the DAIR-V2X dataset substantiate the superiority of our approach. For further insights and resources, our project repository is accessible at https://github.com/MassimoQu/v2i-calib.

Reliable real-time extrinsic parameters of 3D Light Detection and Ranging (LiDAR) and camera are a key component of multi-modal perception systems. However, extrinsic transformation may drift gradually during operation, which can result in decreased accuracy of perception system. To solve this problem, we propose a line-based method that enables automatic online extrinsic calibration of LiDAR and camera in real-world scenes. Herein, the line feature is selected to constrain the extrinsic parameters for its ubiquity. Initially, the line features are extracted and filtered from point clouds and images. Afterwards, an adaptive optimization is utilized to provide accurate extrinsic parameters. We demonstrate that line features are robust geometric features that can be extracted from point clouds and images, thus contributing to the extrinsic calibration. To demonstrate the benefits of this method, we evaluate it on KITTI benchmark with ground truth value. The experiments verify the accuracy of the calibration approach. In online experiments on hundreds of frames, our approach automatically corrects miscalibration errors and achieves an accuracy of 0.2 degrees, which verifies its applicability in various scenarios. This work can provide basis for perception systems and further improve the performance of other algorithms that utilize these sensors.

Mapping and localization in non-static environments are fundamental problems in robotics. Most of previous methods mainly focus on static and highly dynamic objects in the environment, which may suffer from localization failure in semi-dynamic scenarios without considering objects with lower dynamics, such as parked cars and stopped pedestrians. In this paper, we introduce semantic mapping and lifelong localization approaches to recognize semi-dynamic objects in non-static environments. We also propose a generic framework that can integrate mainstream object detection algorithms with mapping and localization algorithms. The mapping method combines an object detection algorithm and a SLAM algorithm to detect semi-dynamic objects and constructs a semantic map that only contains semi-dynamic objects in the environment. During navigation, the localization method can classify observation corresponding to static and non-static objects respectively and evaluate whether those semi-dynamic objects have moved, to reduce the weight of invalid observation and localization fluctuation. Real-world experiments show that the proposed method can improve the localization accuracy of mobile robots in non-static scenarios.