Single Lidar Research Articles

Surface roughness metrology with a raster scanning single photon LiDAR

We explore a novel, to the best of our knowledge, approach to surface roughness metrology utilizing a single pixel, raster scanning single photon counting LiDAR system. It uses a collimated laser beam in picosecond pulses to probe a surface, capturing the changes of back-scattered photons from different points on the surface into a single mode fiber, and counting them using a single photon detector. These back-scattered photons carry speckle noise produced by the rough surface, and the variation in photon counts over different illumination points across the surface becomes a good measure of its roughness. By analyzing the variation frequency as the LiDAR scans over the surface using machine learning techniques, we demonstrate general measurements of surface roughness from 1.21 (1.27±4.51) to 102.01 (87.97±10.55) microns.

Comparing triple and single Doppler lidar wind measurements with sonic anemometer data based on a new filter strategy for virtual tower measurements

Abstract. In this study, we compare the wind measurements of a virtual tower triple Doppler lidar setup to those of a sonic anemometer located at a height of 90 m above ground on an instrumented tower and with those of two single Doppler lidars to evaluate the effect of the horizontal homogeneity assumption used for single Doppler lidar applications on the measurement accuracy. The triple lidar setup was operated in a 90 m stare and a step–stare mode at six heights between 90 and 500 m above ground, while the single lidars were operated in a continuous scan velocity–azimuth display (VAD) mode where one of them had a zenith angle of 54.7° and the other one of 28.0°. The instruments were set up at the boundary-layer field site of the German Meteorological Service (DWD) in July and August of 2020 during the FESST@MOL (Field Experiment on sub-mesoscale spatiotemporal variability at the Meteorological Observatory Lindenberg) 2020 campaign. Overall, we found good agreement of the lidar methods for the whole study period for different averaging times and scan modes compared to the sonic anemometer. For the step–stare mode wind speed measurements, the comparability between the triple lidar and the sonic anemometer was 0.47 m s−1 at an averaging time of 30 min with a bias value of −0.34 m s−1. For wind speed measured by one single lidar setup for the same period with an averaging time of 30 min, we found a comparability of 0.32 m s−1 at an averaging time of 30 min and a bias value of −0.07 m s−1 as well as values of 0.47 and −0.34 m s−1 for the other one, respectively. We also compared the wind velocity measurements of the single and triple lidars at different heights and found decreasing agreement between them with increasing measurement height up to 495 m above ground for the single lidar systems. We found that the single Doppler lidar with the increased zenith angle produced poorer agreement with the triple Doppler lidar setup than the one with the lower zenith angle, especially at higher altitudes. At a height of 495 m above ground and with an averaging time of 30 min the comparability and bias for the larger zenith angle were 0.71 and −0.50 m s−1, respectively, compared to values of 0.57 and −0.28 m s−1 for the smaller zenith angle. Our results confirm that a single Doppler lidar provides reliable wind speed and direction data over heterogeneous but basically flat terrain in different scan configurations. For the virtual tower scanning strategies, we developed a new filtering approach based on a median absolute deviation (MAD) filter combined with a relatively relaxed filtering criterion for the signal-to-noise ratio output by the instrument.

AN EVALUATION OF SOLID-STATE LIDAR FOR LOCALIZATION AND HD POINT CLOUD MAPPING

Abstract. Cost-effective navigation and positioning systems for autonomous vehicles has become a key focus of research in recent years. Having an accurate position within a lane is vital to enabling high levels of automation and improving safety. Traditionally, vehicle navigation and positioning systems have relied heavily on the Global Navigation Satellite System (GNSS), particularly in open-sky scenarios. However, GNSS signals can be easily disrupted by environmental interferences. These include phenomena such as urban canyons, which result from multi-path interferences, as well as challenges posed by Non-Line-of-Sight (NLOS) situations. In the pursuit of developing robust systems resilient to such issues, the concept of sensor fusion has been widely employed. Among all sensors used in commercial self-driving vehicles, mechanical LiDAR is the primary sensor. Utilizing point cloud data from LiDAR and registering it with a prior point cloud map can result in highly accurate position results. However, the high cost of mechanical LiDAR has limited the mass production of point cloud map and autonomous vehicle. In this paper, we evaluate several successful Simultaneous Localization and Mapping (SLAM) architectures from LiDAR-based to LiDAR-Inertial-based using single Solid-State LiDAR (SSL). Last, we proposed a single SSL mapping and localization framework that can achieve 36 centimeters 3D RMSE and 0.5 degree accuracy in heading estimation.

PMLIO: PANORAMIC TIGHTLY-COUPLED MULTI-LIDAR-INERTIAL ODOMETRY AND MAPPING

Abstract. The limited field of view (FoV) of single LiDAR poses challenges for robots to achieve comprehensive environmental perception. Incorporating multiple LiDAR sensors can effectively broaden the FoV of robots, providing abundant measurements to facilitate simultaneous localization and mapping (SLAM). In this paper, we propose a panoramic tightly-coupled multi-LiDAR-inertial odometry and mapping framework, which fully leverages the properties of solid-state LiDAR and spinning LiDAR. The key of the proposed framework lies in the effective completion of multi-LiDAR spatial-temporal fusion. Additionally, we employ the iterated extended Kalman filter to achieve tightly-coupled inertial odometry and mapping with IMU data. PMLIO showcases competitive performance on multiple scenarios data, compared with state-of-the-art single LiDAR-inertial SLAM algorithms, and reaches a noteworthy improvement of 27.1% and 12.9% in max and median of absolute pose error (APE) respectively.

Recent Advances in Light Detection and Ranging: Optical Modulation Solutions and Novel Nanotechnologies

AbstractLight detection and ranging (LiDAR) sensor is widely recognized as a critical component for accurate perception. However, there are a host of challenges that impede their performance, including low spatial resolution, high costs, large size, low reliability, and susceptibility to interference. It is challenging to overcome these issues using a single LiDAR module, necessitating the need for a review of current LiDAR technologies. The paper commences by introducing the fundamental principles of various laser rangefinders and discussing the optical modulation technologies used to prevent interference and ghost images. Next, the paper delves into the latest developments in laser technology, with a focus on enhancing the switching rate, compliance with eye safety regulations, miniaturization, and improving stability. One highly promising innovation is the photonic crystal surface emitting laser (PCSEL), a novel light source that boasts high‐speed, small divergence angles, and high‐power output. Finally, the paper discusses the advancements made in non‐solid‐state scanning and solid‐state scanning, such as improving stability, increasing scanning angles, and optimizing the manufacturing of mechanical and micro‐electromechanical systems (MEMS). Additionally, the paper highlights the recent advancements in nanotechnology, specifically metasurface technology, which offers superior capabilities such as beam deflection, enhanced field‐of‐view (FOV), and dynamic modulation.

MOVIN: Real‐time Motion Capture using a Single LiDAR

AbstractRecent advancements in technology have brought forth new forms of interactive applications, such as the social metaverse, where end users interact with each other through their virtual avatars. In such applications, precise full‐body tracking is essential for an immersive experience and a sense of embodiment with the virtual avatar. However, current motion capture systems are not easily accessible to end users due to their high cost, the requirement for special skills to operate them, or the discomfort associated with wearable devices. In this paper, we present MOVIN, the data‐driven generative method for real‐time motion capture with global tracking, using a single LiDAR sensor. Our autoregressive conditional variational autoencoder (CVAE) model learns the distribution of pose variations conditioned on the given 3D point cloud from LiDAR. As a central factor for high‐accuracy motion capture, we propose a novel feature encoder to learn the correlation between the historical 3D point cloud data and global, local pose features, resulting in effective learning of the pose prior. Global pose features include root translation, rotation, and foot contacts, while local features comprise joint positions and rotations. Subsequently, a pose generator takes into account the sampled latent variable along with the features from the previous frame to generate a plausible current pose. Our framework accurately predicts the performer's 3D global information and local joint details while effectively considering temporally coherent movements across frames. We demonstrate the effectiveness of our architecture through quantitative and qualitative evaluations, comparing it against state‐of‐the‐art methods. Additionally, we implement a real‐time application to showcase our method in real‐world scenarios. MOVIN dataset is available at https://movin3d.github.io/movin_pg2023/https://movin3d.github.io/movin_pg2023/">https://movin3d.github.io/movin_pg2023/.

Tightly coupled laser-inertial pose estimation and map building based on B-spline curves

Simultaneous localization and mapping (SLAM) plays a key role in 3D environment modeling and mobile robot environment perception. However, the traditional discrete-time Laser-inertial SLAM methods are not robust due to the imbalanced registration steps between a single LiDAR frame and the global map. This paper proposes a tightly coupled laser-inertial pose estimation and map building method that uses B-spline curves to represent continuous-time trajectory and achieve high robustness of the registration steps. To ensure efficiency, the proposed method separates the SLAM task into an odometer module and a mapping module. The odometer module performs a coarse pose estimation, while the mapping module performs a fine one and builds a global map with 3D LiDAR points. B-spline curves are utilized to integrate both IMU measurement constraints and LiDAR point constraints in the proposed mapping module, which can enhance the association of consecutive LiDAR frames in the optimization step. Besides, the explicit expression of the Jacobi matrix derivation for B-spline-based laser residuals is also introduced to furtherly improve the computation efficiency. Both indoor and outdoor experiments are conducted on a self-collected dataset and a public dataset. Experimental results show that the proposed method can achieve superior performance than the baseline method LIO-mapping.

An Extended Vector Polar Histogram Method Using Omni-Directional LiDAR Information

This study presents an extended vector polar histogram (EVPH) method for efficient robot navigation using omni-directional LiDAR data. Although the conventional vector polar histogram (VPH) method is a powerful technique suitable for LiDAR sensors, it is limited in its sensing range by the single LiDAR sensor to a semicircle. To address this limitation, the EVPH method incorporates multiple LiDAR sensor’s data for omni-directional sensing. First off, in the EVPH method, the LiDAR sensor coordinate systems are directly transformed into the robot coordinate system to obtain an omni-directional polar histogram. Several techniques are also employed in this process, such as minimum value selection and linear interpolation, to generate a uniform omni-directional polar histogram. The resulting histogram is modified to represent the robot as a single point. Subsequently, consecutive points in the histogram are grouped to construct a symbol function for excluding concave blocks and a threshold function for safety. These functions are combined to determine the maximum cost value that generates the robot’s next heading angle. Robot backward motion is made feasible based on the determined heading angle, enabling the calculation of the velocity vector for time-efficient and collision-free navigation. To assess the efficacy of the proposed EVPH method, experiments were carried out in two environments where humans and obstacles coexist. The results showed that, compared to the conventional method, the robot traveled safely and efficiently in terms of the accumulated amount of rotations, total traveling distance, and time using the EVPH method. In the future, our plan includes enhancing the robustness of the proposed method in congested environments by integrating parameter adaptation and dynamic object estimation methods.

Using spatiotemporal stacks for precise vehicle tracking from roadside 3D LiDAR data

This paper develops a non-model based vehicle tracking methodology for extracting road user trajectories as they pass through the field of view of a 3D LiDAR sensor mounted on the side of the road. To minimize the errors, our work breaks from conventional practice and postpones target segmentation until after collecting LiDAR returns over many scans. Specifically, our method excludes all non-vehicle returns in each scan and retains the ungrouped vehicle returns. These vehicle returns are stored over time in a spatiotemporal stack (ST stack) and we develop a vehicle motion estimation framework to cluster the returns from the ST stack into distinct vehicles and extract their trajectories. This processing includes removing the impacts of the target's changing orientation relative to the LiDAR sensor while separately taking care to preserve the crisp transition to/from a stop that would normally be washed out by conventional data smoothing or filtering. This proof of concept study develops the methodology using a single LiDAR sensor, thus, limiting the surveillance region to the effective range of the given sensor. It should be clear from the presentation that, provided sufficient georeferencing, the surveillance region can be extended indefinitely by deploying multiple LiDAR sensors with overlapping fields of view.

Weakly Supervised 3D Multi-Person Pose Estimation for Large-Scale Scenes Based on Monocular Camera and Single LiDAR

Depth estimation is usually ill-posed and ambiguous for monocular camera-based 3D multi-person pose estimation. Since LiDAR can capture accurate depth information in long-range scenes, it can benefit both the global localization of individuals and the 3D pose estimation by providing rich geometry features. Motivated by this, we propose a monocular camera and single LiDAR-based method for 3D multi-person pose estimation in large-scale scenes, which is easy to deploy and insensitive to light. Specifically, we design an effective fusion strategy to take advantage of multi-modal input data, including images and point cloud, and make full use of temporal information to guide the network to learn natural and coherent human motions. Without relying on any 3D pose annotations, our method exploits the inherent geometry constraints of point cloud for self-supervision and utilizes 2D keypoints on images for weak supervision. Extensive experiments on public datasets and our newly collected dataset demonstrate the superiority and generalization capability of our proposed method. Project homepage is at \url{https://github.com/4DVLab/FusionPose.git}.

LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensors.

We propose a multi-sensor fusion method for capturing challenging 3D human motions with accurate consecutive local poses and global trajectories in large-scale scenarios, only using single LiDAR and 4 IMUs, which are set up conveniently and worn lightly. Specifically, to fully utilize the global geometry information captured by LiDAR and local dynamic motions captured by IMUs, we design a two-stage pose estimator in a coarse-to-fine manner, where point clouds provide the coarse body shape and IMU measurements optimize the local actions. Furthermore, considering the translation deviation caused by the view-dependent partial point cloud, we propose a pose-guided translation corrector. It predicts the offset between captured points and the real root locations, which makes the consecutive movements and trajectories more precise and natural. Moreover, we collect a LiDAR-IMU multi-modal mocap dataset, LIPD, with diverse human actions in long-range scenarios. Extensive quantitative and qualitative experiments on LIPD and other open datasets all demonstrate the capability of our approach for compelling motion capture in large-scale scenarios, which outperforms other methods by an obvious margin. We will release our code and captured dataset to stimulate future research.

Flexible sensor concept and an efficient integrated sensing controlling for an efficient human-robot collaboration using 3D local global sensing systems.

Human-robot collaboration with traditional industrial robots is a cardinal step towards agile manufacturing and re-manufacturing processes. These processes require constant human presence, which results in lower operational efficiency based on current industrial collision avoidance systems. The work proposes a novel local and global sensing framework, which discusses a flexible sensor concept comprising a single 2D or 3D LiDAR while formulating occlusion due to the robot body. Moreover, this work extends the previous local global sensing methodology to incorporate local (co-moving) 3D sensors on the robot body. The local 3D camera faces toward the robot occlusion area, resulted from the robot body in front of a single global 3D LiDAR. Apart from the sensor concept, this work also proposes an efficient method to estimate sensitivity and reactivity of sensing and control sub-systems The proposed methodologies are tested with a heavy-duty industrial robot along with a 3D LiDAR and camera. The integrated local global sensing methods allow high robot speeds resulting in process efficiency while ensuring human safety and sensor flexibility.

Error Analysis for Shipborne-based-based Single Band LiDAR Point Cloud Data

A new measuring technique called a ship-based single band LiDAR system is utilized to swiftly capture underwater terrain to employ dense and precise point cloud data on a vast region for bathymetry. The accuracy of point clouds is strongly impacted by the ease with which numerous error sources might affect shipborne single-band lidar measurement systems. Shipborne-based single-band LiDAR point cloud errors are mostly caused by GPS, attitude angle, range, and integration errors. This research explores and examines the impact of the scanning angle and attitude angle on bathymetry because they can fluctuate significantly throughout the actual measurement process. The point cloud errors resulting from numerous sets of scanning angle and attitude-angle data sets are calculated. The linear relationship and positioning accuracy between the three are intuitively depicted by comparable curves, presuming that other error causes are quantifiable. Analysis reveals that the error will also rise as the scanning angle increases.Additionally, the roll angle inaccuracy in the attitude angle has a minimal impact on the accuracy of the plane but a substantial impact on the accuracy of the elevation. Elevation error significantly impacts plane positioning accuracy but less on elevation accuracy. Positioning is only minimally affected by yaw angle inaccuracy.

OSM-SLAM: Aiding SLAM with OpenStreetMaps priors.

In the last decades, Simultaneous Localization and Mapping (SLAM) proved to be a fundamental topic in the field of robotics, due to the many applications, ranging from autonomous driving to 3D reconstruction. Many systems have been proposed in literature exploiting a heterogeneous variety of sensors. State-of-the-art methods build their own map from scratch, using only data coming from the equipment of the robot, and not exploiting possible reconstructions of the environment. Moreover, temporary loss of data proves to be a challenge for SLAM systems, as it demands efficient re-localization to continue the localization process. In this paper, we present a SLAM system that exploits additional information coming from mapping services like OpenStreetMaps, hence the name OSM-SLAM, to face these issues. We extend an existing LiDAR-based Graph SLAM system, ART-SLAM, making it able to integrate the 2D geometry of buildings in the trajectory estimation process, by matching a prior OpenStreetMaps map with a single LiDAR scan. Each estimated pose of the robot is then associated with all buildings surrounding it. This association allows to improve localization accuracy, but also to adjust possible mistakes in the prior map. The pose estimates coming from SLAM are then jointly optimized with the constraints associated with the various OSM buildings, which can assume one of the following types: Buildings are always fixed (Prior SLAM); buildings surrounding a robot are movable in chunks, for every scan (Rigid SLAM); and every single building is free to move independently from the others (Non-rigid SLAM). Lastly, OSM maps can also be used to re-localize the robot when sensor data is lost. We compare the accuracy of the proposed system with existing methods for LiDAR-based SLAM, including the baseline, also providing a visual inspection of the results. The comparison is made by evaluating the estimated trajectory displacement using the KITTI odometry dataset. Moreover, the experimental campaign, along with an ablation study on the re-localization capabilities of the proposed system and its accuracy in loop detection-denied scenarios, allow a discussion about how the quality of prior maps influences the SLAM procedure, which may lead to worse estimates than the baseline.

Exploiting Multi-Modal Fusion for Urban Autonomous Driving Using Latent Deep Reinforcement Learning

Human driving decisions are the leading cause of road fatalities. Autonomous driving naturally eliminates such incompetent decisions and thus can improve traffic safety and efficiency. Deep reinforcement learning (DRL) has shown great potential in learning complex tasks. Recently, researchers investigated various DRL-based approaches for autonomous driving. However, exploiting multi-modal fusion to generate perception and motion prediction and then leveraging these predictions to train a latent DRL has not been targeted yet. To that end, we propose enhancing urban autonomous driving using multi-modal fusion with latent DRL. A single LIDAR sensor is used to extract bird's-eye view (BEV), range view (RV), and residual input images. These images are passed into LiCaNext, a real-time multi-modal fusion network, to produce accurate joint perception and motion prediction. Next, predictions are fed with another simple BEV image into the latent DRL to learn a complex end-to-end driving policy ensuring safety, efficiency, and comfort. A sequential latent model is deployed to learn more compact representations from inputs, leading to improved sampling efficiency for reinforcement learning. Our experiments are simulated on CARLA and evaluated against state-of-the-art DRL models. Results manifest that our method learns a better driving policy that outperforms other prevailing models. Further experiments are conducted to reveal the effectiveness of our proposed approach under different environments and varying weather conditions.

Real-Time 3D Mapping in Complex Environments Using a Spinning Actuated LiDAR System

LiDAR is a crucial sensor for 3D environment perception. However, limited by the field of view of the LiDAR, it is sometimes difficult to achieve complete coverage of the environment with a single LiDAR. In this paper, we designed a spinning actuated LiDAR mapping system that is compatible with both UAV and backpack platforms and propose a tightly coupled laser–inertial SLAM algorithm for it. In our algorithm, edge and plane features in the point cloud are first extracted. Then, for the significant changes in the distribution of point cloud features between two adjacent scans caused by the continuous rotation of the LiDAR, we employed an adaptive scan accumulation method to improve the stability and accuracy of point cloud registration. After feature matching, the LiDAR feature factors and IMU pre-integration factor are added to the factor graph and jointly optimized to output the trajectory. In addition, an improved loop closure detection algorithm based on the Cartographer algorithm is used to reduce the drift. We conducted exhaustive experiments to evaluate the performance of the proposed algorithm in complex indoor and outdoor scenarios. The results showed that our algorithm is more accurate than the state-of-the-art algorithms LIO-SAM and FAST-LIO2 for the spinning actuated LiDAR system, and it can achieve real-time performance.

A Lightweight LiDAR-Camera Sensing Method of Obstacles Detection and Classification for Autonomous Rail Rapid Transit

Detecting obstacles accurately is a vital part of realizing autonomous driving. But utilizing single sensors, for example, RGB camera or LiDAR itself, is difficult to meet the needs of autonomous driving under the condition of sparse environmental sensing data. In contrast, equipments that can collect data in large quantities are expensive and difficult to be utilized in practical applications. Hence, under the premise of ensuring low application costs, the difficult task is to obtain high-precision three-dimensional information of obstacles in real-time from sparse data. To overcome this problem, a real-time detection technique that combines LiDAR data and image information for obstacles detection is proposed in this article. It can detect vehicles close to the ART in sparse data in real-time. The sparse point cloud is de-noised initially and the ground is separated from the initial cloud data. The remained cloud, which includes surrounding vehicles, are processed by the clustering algorithm according to the structural relationship and scanning mechanism of the LiDAR. Then the processed LiDAR data is fused with the classification information generated from images based on a neural network to locate suspicious obstacles. The ratio values between the central values of vehicles and image width are obtained in the image coordinate system. Then the category information and ratio values are mapped on the processed LiDAR clusters during ART operation. The method is tested and verified in the real field. The evaluation results show that the proposed method can achieve an average accuracy of 85.5% for obstacle classification with a cost-effective sensing suit.

Dynamic Load Regulation of Robots with Multi-sensor Fusion

Multi-sensor fusion technology has been extensively deployed for the autonomous navigation and control of robots. Theoretically, by increasing the number of sensors, more effective data on the surrounding environment and obstacle information can be obtained. However, in practical applications, a massive number of sensors will not only increase the cost of using sensors in the system but also add to the difficulty and cost of data processing, leading to unnecessary resource consumption. To solve this problem, first, the robot navigation area is divided according to the importance of the navigation area and the characteristics of sensor parameters. The navigation non-core area adopts the single lidar sensor, while the core area adopts the multi-sensor fusion of “lidar +” to reduce the number of sensors used in the fusion and improve the performance of multi-sensor fusion after the division. Then, in consideration of the higher requirement for computing capacity of the device imposed by deep learning, the multi-sensor fusion algorithm is introduced into the dynamic load regulation mechanism to solve the problem from the algorithm level so that the outperformance of deep learning in feature extraction and other aspects can be utilized to reduce the performance requirements of the device and the overall power consumption of the system. Finally, by building the ROS experimental platform, it is verified that the robot can use autonomous navigation in complex and diversified home scenes.

LiDAR-Based Global Localization Using Histogram of Orientations of Principal Normals

Global localization on LiDAR point cloud maps is a challenging task because of the sparse nature of point clouds and the large size difference between LiDAR scans and the maps. In this paper, we solve the LiDAR-based global localization problem based upon the plane-motion assumption. We first project the clouds into Bird’s-eye View (BV) images and transform the problem into a BV image matching problem. We then introduce a novel local descriptor, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , Histogram of Orientations of Principal Normals (HOPN), to perform matching. The HOPN descriptor encodes the point normals of the clouds, and is more effective in matching BV images than the common image descriptors. In addition, we present the consensus set maximization algorithm to robustly estimate a rigid pose from the HOPN matches in the case of the low inlier ratio. The experimental results on three large-scale datasets show that our method achieves state-of-the-art global localization performance when using either single LiDAR scans or local maps.

Mapping method of single LiDAR for indoor degraded environment

Aiming at the problem that the mobile robot equipped with only a single-line LiDAR sensor is highly prone to mapping errors in the indoor degraded environment with long corridors, this paper proposes a Cartographer-SLAM based method to overcome the failure of laser odometer in problematic scenarios and construct high-precision raster maps. The method consists of two main parts. First, corridor detector for environment sensing (CDES) endows the mobile platform with the ability to perceive degraded environment through real-time obstacle monitoring and robust linear feature extraction. Second, specific observations for pose optimization (SOPO), additional observations are introduced for pose correction by measuring the variation of wall spacing. Experiment results demonstrate that the proposed method can overcome the failure problem of laser odometer in the long featureless corridor, and the area error of map building is 0.63% in the corridor environment with a total length of 45.15 m.