Multi-lidar System Research Articles

Multi-LiDAR human joint recognition algorithm in hospital wards based on improved V2V-Posenet

To prevent convulsions and falls of patients in the absence of medical staff, it is crucial to monitor their physical condition in hospital wards. However, several unresolved challenges in human joint recognition remain, such as object occlusion, human self-occlusion and complex backgrounds, resulting in difficulties in its practical application. In this paper, a multi-LiDAR system is proposed to obtain a multi-view human body point cloud. An improved V2V-Posenet model was introduced to detect the actual position of the human joint. In this system, each point cloud was spliced into a full point cloud and voxelized into the model. We also used a random voxel zero setting for data enhancement, constraining the relative length between human joints into a loss function and three-dimensional Gaussian filtering in a heat map for model learning. The improved model exhibited excellent performance in detecting human joints in hospital wards. The experimental results showed that the improved model achieved 91.6 % mean average precision, compared to 80.1 % for the original model and 77.4 % for the comparison algorithm A2J-Posenet. The speed of the improved model meets the requirements for real-time target detection.

Measurement report: Nocturnal subsidence behind the cold front enhances surface particulate matter in plains regions: observations from the mobile multi-lidar system

Abstract. A multi-lidar system, mounted in a vehicle to monitor the profiles of temperature, wind, and particle optical properties, was utilized to investigate the winter fine particulate matter (PM2.5) pollution from a vertical perspective in four cities in China in winter 2018. We observed the enhancement of surface nocturnal PM2.5 in two typical plains cities (Changzhou and Wangdu), which was attributed to the subsidence of PM2.5 transported from upstream polluted areas, with the wind turning north and downdrafts dominating. Combining the observed surface PM2.5, the reanalysis meteorological data, and the GEOS-Chem model simulation, we revealed the transport nocturnal PM2.5 enhancement by subsidence (T-NPES) events that occurred frequently in the two cities, with percentages of 12.2 % and 18.0 %, respectively, during December 2018–February 2019. Furthermore, the GEOS-Chem model simulation further confirmed the ubiquity of winter T-NPES events on a large scale, including North China Plain and the Yangtze River Delta. Process analysis revealed that the subsidence was closely correlated with the southeasterly movement of the high-pressure system and the passage of the cold front, resulting in the increase of temperature aloft, a stronger inversion layer, and further PM2.5 accumulation in the atmospheric boundary layer. Thus, a conceptual model of the T-NPES events was proposed to highlight this surface PM2.5 enhancement mechanism in these plains regions. However, it did not apply to the two cities in the basin region (Xi'an and Chengdu) due to the obstruction of the weather system movement by the mountains surrounding the basin.

Extrinsic calibration for multi-LiDAR systems involving heterogeneous laser scanning models.

In the realm of autonomous driving, there is a pressing demand for heightened perceptual capabilities, giving rise to a plethora of multisensory solutions. Among these, multi-LiDAR systems have gained significant popularity. Within the spectrum of available combinations, the integration of repetitive and non-repetitive LiDAR configurations emerges as a balanced approach, offering a favorable trade-off between sensing range and cost. However, the calibration of such systems remains a challenge due to the diverse nature of point clouds, low-common-view, and distinct densities. This study proposed a novel targetless calibration algorithm for extrinsic calibration between Hybrid-Solid-State-LiDAR(SSL) and Mechanical-LiDAR systems, each employing different scanning modes. The algorithm harnesses planar features within the scene to construct matching costs, while proposing the adoption of the Gaussian Mixture Model (GMM) to address outliers, thereby mitigating the issue of overlapping points. Dynamic trust-region-based optimization is incorporated during iterative processes to enhance nonlinear convergence speed. Comprehensive evaluations across diverse simulated and real-world scenarios affirm the robustness and precision of our algorithm, outperforming current state-of-the-art methods.

Multi-Lidar System Localization and Mapping with Online Calibration

Currently, the demand for automobiles is increasing, and daily travel is increasingly reliant on cars. However, accompanying this trend are escalating traffic safety issues. Surveys indicate that most traffic accidents stem from driver errors, both intentional and unintentional. Consequently, within the framework of vehicular intelligence, intelligent driving uses computer software to assist drivers, thereby reducing the likelihood of road safety incidents and traffic accidents. Lidar, an essential facet of perception technology, plays an important role in vehicle intelligent driving. In real-world driving scenarios, the detection range of a single laser radar is limited. Multiple laser radars can improve the detection range and point density, effectively mitigating state estimation degradation in unstructured environments. This, in turn, enhances the precision and accuracy of synchronous positioning and mapping. Nonetheless, the relationship governing pose transformation between multiple lidars is intricate. Over extended periods, perturbations arising from vibrations, temperature fluctuations, or collisions can compromise the initially converged external parameters. In view of these concerns, this paper introduces a system capable of concurrent multi-lidar positioning and mapping, as well as real-time online external parameter calibration. The method first preprocesses the original measurement data, extracts linear and planar features, and rectifies motion distortion. Subsequently, leveraging degradation factors, the convergence of the multi-lidar external parameters is detected in real time. When deterioration in external parameters is identified, the local map of the main laser radar and the feature point cloud of the auxiliary laser radar are associated to realize online calibration. This is succeeded by frame-to-frame matching according to the converged external parameters, culminating in laser odometer computation. Introducing ground constraints and loop closure detection constraints in the back-end optimization effectuates global estimated pose rectification. Concurrently, the feature point cloud is aligned with the global map, and map update is completed. Finally, experimental validation is conducted on data acquired from Chang’an University to substantiate the system’s online calibration and positioning mapping accuracy, robustness, and real-time performance.

D-LIOM: Tightly-Coupled Direct LiDAR-Inertial Odometry and Mapping

Simultaneous localization and mapping via LiDAR-Inertial fusion is a crucial technology in many automation-related applications. Recently, a number of approaches based on geometric features have evolved, yielding impressive results via tightly-coupled estimation. This sort of feature-based techniques, however, are inextricably linked to the scanning mechanism of the LiDAR, relying on stable feature detection, and thus are difficult to adapt to multi-LiDAR systems. A few direct solutions, on the other hand, register the raw point cloud with the built probability map, which is more computationally efficient and easy to be extended. But, the existing direct approaches are all loosely-coupled, lacking correction of the IMU biases, and thus only work well in 2D cases. To this end, we present D-LIOM, a tightly-coupled Direct LiDAR-Inertial Odometry and Mapping framework. In D-LIOM, a scan is directly registered to a probability submap, and the LiDAR odometry, the IMU pre-integration, and the gravity constraint are integrated to build a local factor graph in the submap's time window, allowing the system to perform real-time high-precision pose estimation. Furthermore, to eliminate accumulated errors in time, we detect loops and adjust the sparse pose graph based on mutual matching of projected 2D submaps, allowing D-LIOM to run stably in large-scale scenes. In addition, to improve its flexibility to varied sensor combinations, D-LIOM supports multi-LiDAR inputs and facilitates the initialization with a common 6-axis IMU. Extensive experiments demonstrate that D-LIOM largely outperforms the existing state-of-the-art counterparts in mapping effect and localization accuracy as well as with high time efficiency. Lastly, to ensure that our results are entirely reproducible, all necessary data and codes are made open-source available. One introduction video can also be found on the online website.

Robust Odometry and Mapping for Multi-LiDAR Systems With Online Extrinsic Calibration

Combining multiple LiDARs enables a robot to maximize its perceptual awareness of environments and obtain sufficient measurements, which is promising for simultaneous localization and mapping (SLAM). This article proposes a system to achieve robust and simultaneous extrinsic calibration, odometry, and mapping for multiple LiDARs. Our approach starts with measurement preprocessing to extract edge and planar features from raw measurements. After a motion and extrinsic initialization procedure, a sliding window-based multi-LiDAR odometry runs onboard to estimate poses with an online calibration refinement and convergence identification. We further develop a mapping algorithm to construct a global map and optimize poses with sufficient features together with a method to capture and reduce data uncertainty. We validate our approach’s performance with extensive experiments on 10 sequences (4.60-km total length) for the calibration and SLAM and compare it against the state of the art. We demonstrate that the proposed work is a complete, robust, and extensible system for various multi-LiDAR setups. The source code, datasets, and demonstrations are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://ram-lab.com/file/site/m-loam</uri> .

A Pedestrian Detection Algorithm Based on Score Fusion for Multi-LiDAR Systems.

Pedestrian detection plays an essential role in the navigation system of autonomous vehicles. Multisensor fusion-based approaches are usually used to improve detection performance. In this study, we aimed to develop a score fusion-based pedestrian detection algorithm by integrating the data of two light detection and ranging systems (LiDARs). We first evaluated a two-stage object-detection pipeline for each LiDAR, including object proposal and fine classification. The scores from these two different classifiers were then fused to generate the result using the Bayesian rule. To improve proposal performance, we applied two features: the central points density feature, which acts as a filter to speed up the process and reduce false alarms; and the location feature, including the density distribution and height difference distribution of the point cloud, which describes an object’s profile and location in a sliding window. Extensive experiments tested in KITTI and the self-built dataset show that our method could produce highly accurate pedestrian detection results in real-time. The proposed method not only considers the accuracy and efficiency but also the flexibility for different modalities.

Turbulence Measurements with Dual-Doppler Scanning Lidars

Velocity-component variances can be directly computed from lidar measurements using information of the second-order statistics within the lidar probe volume. Specifically, by using the Doppler radial velocity spectrum, one can estimate the unfiltered radial velocity variance. This information is not always available in current lidar campaigns. The velocity-component variances can also be indirectly computed from the reconstructed velocities but they are biased compared to those computed from, e.g., sonic anemometers. Here we show, for the first time, how to estimate such biases for a multi-lidar system and we demonstrate, also for the first time, their dependence on the turbulence characteristics and the lidar beam scanning geometry relative to the wind direction. For a dual-Doppler lidar system, we also show that the indirect method has an advantage compared to the direct one for commonly-used scanning configurations due to the singularity of the system. We demonstrate that our estimates of the radial velocity and velocity-component biases are accurate by analysis of measurements performed over a flat site using a dual-Doppler lidar system, where both lidars stared over a volume close to a sonic anemometer at a height of 100 m. We also show that mapping these biases over a spatial domain helps to plan meteorological campaigns, where multi-lidar systems can potentially be used. Particularly, such maps help the multi-point mapping of wind resources and conditions, which improve the tools needed for wind turbine siting.

Calibration of Multi-LIDAR Systems: Application to Bucket Wheel Reclaimers

This paper presents a method to calibrate the extrinsic parameters of light detection and rangings (LIDARs) mounted on a bucket-wheel reclaimer (BWR). BWR’s are widely used for stacking and reclaiming bulk materials in stockyards. Current BWR systems are either manually operated, remotely operated, or semiautomated using the real-time point cloud data generated by one or more LIDARs. The accurate calibration is of crucial importance as the accuracy of the earth frame point cloud data depends on it. The automated calibration is also a prerequisite for fully autonomous BWR control. The calibration of BWR systems is more difficult than many other LIDAR systems because of their limited pose variation capabilities and the environmental constraints of stockyards. This paper analyzes the problem, presents observability conditions, presents a method to estimate the calibration parameters, and discusses the challenges involved with BWR systems. The results demonstrate subdecimeter accuracy.