# Multi-Lidar System Localization and Mapping with Online Calibration
## 1 Introduction  
Intelligent vehicles rely on key technologies such as environmental perception, intelligent decision-making, control execution, and system design technology [1; 2]. Among these, positioning technology underpins the functionalities of each module. Only with precise knowledge of the vehicle's position and orientation in the current environment can relabile information be provided for perception and planning decisions [3; 4; 5]. In addition to GPS for self-driving vehicles' positioning, two sensor types, namely lidar and cameras, are also widely used in positioning technology [6]. Compared to other sensors, lidar offers the advantages of an extended detection range, immunity to adverseweather conditions, and depth information [7; 8]. Furthermore, it is prominently featured in cutting-edge research [9]. In lidar positioning, two schemes prevail: multiple lidar and single lidar [10]. Although a single multi-line lidar yields rich information, inherent blind spots may arise due to installation locations, leading to relatively sparse point cloud scans in some areas and close ranges [11; 12; 13]. To address these limitations, self-driving vehicles are equipped with multiple laser radars employing low scanning lines. This mitigates issues of blind spots and sparse point clouds found in single laser radars, while considering cost-effectiveness. Lidars with low scanline resolution are economically advantageous over their high scanline resolution counterparts [14; 15]. Therefore, building upon the foundation of multi-lidar systems, this paper introduces a system capable of concurrent multi-lidar positioning, mapping, and real-time external parameter calibration to meet the high-precision positioning demands of unmanned vehicles. Multi-lidar extrinsic calibration is generally categorized into two methods: reference markers and motion-based [16]. The method based on reference markers centers on point cloud registration, typically involving feature extraction for matching or minimizing spatial distances between point clouds. [PERSON] [17] and others propose using two lidars to measure structured scenes from different perspectives, conducting multiple repeated scans by altering the pose of the sensor carrier, extracting at least three pairs of planes, and forming constraints to determine external parameters. This method obviates the need for artificial calibration boards, relying solely on walls and floors. [PERSON] and [PERSON] [18] extract 3D-HARIS key points from original point cloud data, use the histogram of orientation (SHOT) feature descriptor for characterization, and employ a random sample consensus algorithm for point cloud registration. Feature descriptors construct vectors, gauging similarity through vector subtraction and modulus. [PERSON] and [PERSON] et al. [19] select three linearly independent corner planes, extract the corresponding plane features through the RANSAC algorithm, retain the largest three planes based on the number of feature points, match plane features and normal vectors of the two lidars, and solve the displacement vector via the least squares method along with the rotation matrix through the Kabsch algorithm [20]. Following the acquisition of the initial external parameters, a nonlinear optimization model is established and iterated until convergence, yielding precise extrinsic results. In natural environments, dedicated reference markers for calibration are often elusive, necessitating motion-based calibration. The motion-based method: [PERSON] introduces a target-free calibration algorithm that assumes consistency among local 3D models scanned by multiple lidars then reconstructs the point cloud from both auxiliary and main lidars during movement [21; 22; 23]. These maps are registered until the algorithm converges. [PERSON] and [PERSON] [24] propose generating common visible features by overlapping common-view fields of view through motion, facilitating mapping and registration of all lidars to acquire reference radar pose and point cloud maps for all radars. [PERSON] et al.