3D Light Detection And Ranging Research Articles

Comprehensive Performance Evaluation between Visual SLAM and LiDAR SLAM for Mobile Robots: Theories and Experiments

SLAM (Simultaneous Localization and Mapping), primarily relying on camera or LiDAR (Light Detection and Ranging) sensors, plays a crucial role in robotics for localization and environmental reconstruction. This paper assesses the performance of two leading methods, namely ORB-SLAM3 and SC-LeGO-LOAM, focusing on localization and mapping in both indoor and outdoor environments. The evaluation employs artificial and cost-effective datasets incorporating data from a 3D LiDAR and an RGB-D (color and depth) camera. A practical approach is introduced for calculating ground-truth trajectories and during benchmarking, reconstruction maps based on ground truth are established. To assess the performance, ATE and RPE are utilized to evaluate the accuracy of localization; standard deviation is employed to compare the stability during the localization process for different methods. While both algorithms exhibit satisfactory positioning accuracy, their performance is suboptimal in scenarios with inadequate textures. Furthermore, 3D reconstruction maps established by the two approaches are also provided for direct observation of their differences and the limitations encountered during map construction. Moreover, the research includes a comprehensive comparison of computational performance metrics, encompassing Central Processing Unit (CPU) utilization, memory usage, and an in-depth analysis. This evaluation revealed that Visual SLAM requires more CPU resources than LiDAR SLAM, primarily due to additional data storage requirements, emphasizing the impact of environmental factors on resource requirements. In conclusion, LiDAR SLAM is more suitable for the outdoors due to its comprehensive nature, while Visual SLAM excels indoors, compensating for sparse aspects in LiDAR SLAM. To facilitate further research, a technical guide was also provided for the researchers in related fields.

System for Detecting Moving Objects Using 3D Li-DAR Technology

The “System for Detecting Moving Objects Using 3D LiDAR Technology” introduces a groundbreaking method for precisely identifying and tracking dynamic entities within a given environment. By harnessing the capabilities of advanced 3D LiDAR (Light Detection and Ranging) technology, the system constructs intricate three-dimensional maps of the surroundings using laser beams. Through continuous analysis of the evolving data, the system adeptly discerns and monitors the movement of objects within its designated area. What sets this system apart is its innovative integration of a multi-sensor approach, combining LiDAR data with inputs from various other sensor modalities. This fusion of data not only enhances accuracy but also significantly boosts the system’s adaptability, ensuring robust performance even in challenging environmental conditions characterized by low visibility or erratic movement patterns. This pioneering approach fundamentally improves the precision and reliability of moving object detection, thereby offering a valuable solution for a diverse array of applications, including autonomous vehicles, surveillance, and robotics.

High precision rail surface obstacle detection algorithm based on 3D imaging LiDAR

Railway perimeter intrusion detection is of great significance for ensuring railway safety operations. In this paper, a Light Detection and Ranging (LiDAR) system is designed and developed based on the specific environmental requirements of railway applications. The system is capable of autonomously adjusting the imaging range and angular resolution, enabling high-precision imaging within a 50 m monitoring area. Additionally, based on registration and a series of filtering algorithms, a railway surface obstacle extraction algorithm is developed. The algorithm achieves a 100 % detection rate for obstacles with a minimum height of 15 cm at any position within the monitoring area, and a 70 % detection rate for obstacles with a height of 10 cm. In this algorithm, a coarse registration method based on global planar features combined with the Iterative Closest Point (ICP) registration algorithm achieves sub-centimeter registration accuracy for consecutive point cloud frames. The filtering algorithm based on local planar features enables precise extraction of geometric changes in the railway surface, with simple calculations and fast processing speed, making it highly valuable for extracting object edge features.

Privacy-preserving pedestrian tracking with path image inpainting and 3D point cloud features

Tracking pedestrian flow in large public areas is vital, yet ensuring privacy is paramount. Traditional visual-based tracking systems are raising concerns for potentially obtaining persistent and permanent identifiers that can compromise individual identities. Moreover, in areas such as the vicinity of restrooms, any form of data acquisition capturing human behavior should be refrained from, making it also crucial to appropriately address and complement these blind spots for a comprehensive analysis of pedestrian movement in the entire area. In this paper, we present our pedestrian tracking algorithm using distributed 3D LiDARs (Light Detection and Ranging), which capture pedestrians as 3D point clouds, omitting identifiable features. Our system bridges blind spots by leveraging historical movement data and 3D point cloud features, complemented by a generative diffusion model to predict trajectories in unseen areas. In a large-scale testbed with 70 LiDARs, the system achieved a 0.98 F-measure, highlighting its potential as a leading privacy-preserving tracking solution.

3-D LiDAR Localization Based on Novel Nonlinear Optimization Method for Autonomous Ground Robot

3D Light Detection and Ranging (LiDAR)-based localization for autonomous ground robot in unknown environment is a critical ability, which has been extensively studied. In this paper, we propose a novel nonlinear optimization method to promote the 3D LiDAR localization performance. Firstly, due to that the objective function is the summation of point correspondence matching error items, a novel item quality evaluation criterion is proposed. The quality of each item is directly proportional to the degree of its matching error distance descent during the optimization process. Then, an attention mechanism based on the criterion is proposed and the objective function is iteratively refined by putting different attention weights on the matching error items. Thirdly, in the Gauss-Newton optimization framework for LiDAR localization, we point out that the Hessian matrix is essentially the sum of the Hessian matrices of high-quality and low-quality point correspondence subsets. To increase the dominance of the Hessian matrix derived from the high-quality point correspondences, the Hessian matrix of the estimated high-quality point correspondence subset in the last updating step is added to the current Hessian matrix. The proposed LiDAR localization is extensively evaluated on public and custom datasets. The experimental results demonstrate that the proposed mechanisms can effectively promote the LiDAR localization performance. We will make our source code open to serve as a new baseline for the robotic community.

MARS-LVIG dataset: A multi-sensor aerial robots SLAM dataset for LiDAR-visual-inertial-GNSS fusion

In recent years, advancements in Light Detection and Ranging (LiDAR) technology have made 3D LiDAR sensors more compact, lightweight, and affordable. This progress has spurred interest in integrating LiDAR with sensors such as Inertial Measurement Units (IMUs) and cameras for Simultaneous Localization and Mapping (SLAM) research. Public datasets covering different scenarios, platforms, and viewpoints are crucial for multi-sensor fusion SLAM studies, yet most focus on handheld or vehicle-mounted devices with front or 360-degree views. Data from aerial vehicles with downward-looking views is scarce, existing relevant datasets usually feature low altitudes and are mostly limited to small campus environments. To fill this gap, we introduce the Multi-sensor Aerial Robots SLAM dataset (MARS-LVIG dataset), providing unique aerial downward-looking LiDAR-Visual-Inertial-GNSS data with viewpoints from altitudes between 80 m and 130 m. The dataset not only offers new aspects to test and evaluate existing SLAM algorithms, but also brings new challenges which can facilitate researches and developments of more advanced SLAM algorithms. The MARS-LVIG dataset contains 21 sequences, acquired across diversified large-area environments including an aero-model airfield, an island, a rural town, and a valley. Within these sequences, the UAV has speeds varying from 3 m/s to 12 m/s, a scanning area reaching up to 577,000 m2, and the max path length of 7.148 km in a single flight. This dataset encapsulates data collected by a lightweight, hardware-synchronized sensor package that includes a solid-state 3D LiDAR, a global-shutter RGB camera, IMUs, and a raw message receiver of the Global Navigation Satellite System (GNSS). For algorithm evaluation, this dataset releases ground truth of both localization and mapping, which are acquired by on-board Real-time Kinematic (RTK) and DJI L1 (post-processed by its supporting software DJI Terra), respectively. The dataset can be downloaded from: https://mars.hku.hk/dataset.html .

LiDAR-based SLAM for robotic mapping: state of the art and new frontiers

PurposeIn recent decades, the field of robotic mapping has witnessed widespread research and development in light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) techniques. This paper aims to provide a significant reference for researchers and engineers in robotic mapping.Design/methodology/approachThis paper focused on the research state of LiDAR-based SLAM for robotic mapping as well as a literature survey from the perspective of various LiDAR types and configurations.FindingsThis paper conducted a comprehensive literature review of the LiDAR-based SLAM system based on three distinct LiDAR forms and configurations. The authors concluded that multi-robot collaborative mapping and multi-source fusion SLAM systems based on 3D LiDAR with deep learning will be new trends in the future.Originality/valueTo the best of the authors’ knowledge, this is the first thorough survey of robotic mapping from the perspective of various LiDAR types and configurations. It can serve as a theoretical and practical guide for the advancement of academic and industrial robot mapping.

Fine-Scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data.

Quantifying the relationship between light and stands or individual trees is of great significance in understanding tree competition, improving forest productivity, and comprehending ecological processes. However, accurately depicting the spatiotemporal variability of light under complex forest structural conditions poses a challenge, especially for precise forest management decisions that require a quantitative study of the relationship between fine-scale individual tree structure and light. 3D RTMs (3-dimensional radiative transfer models), which accurately characterize the interaction between solar radiation and detailed forest scenes, provide a reliable means for depicting such relationships. This study employs a 3D RTM and LiDAR (light detection and ranging) data to characterize the light environment of larch plantations at a fine spatiotemporal scale, further investigating the relationship between absorbed photosynthetically active radiation (APAR) and forest structures. The impact of specific tree structural parameters, such as crown diameter, crown area, crown length, crown ratio, crown volume, tree height, leaf area index, and a distance parameter assessing tree competition, on the daily-scale cumulative APAR per tree was investigated using a partial least squares regression (PLSR) model. Furthermore, variable importance in projection (VIP) was also calculated from the PLSR. The results indicate that among the individual tree structure parameters, crown volume is the most important one in explaining individual tree APAR (VIP = 4.19), while the competition from surrounding trees still plays a role in explaining individual tree APAR to some extent (VIP = 0.15), and crown ratio contributes the least (VIP = 0.03). Regarding the spatial distribution of trees, the average cumulative APAR per tree of larch plots does not increase with an increase in canopy gap fraction. Tree density and average cumulative APAR per tree were fitted using a natural exponential equation, with a coefficient of determination (R2 = 0.89), and a small mean absolute percentage error (MAPE = 0.03). This study demonstrates the potential of combining 3D RTM with LiDAR data to quantify fine-scale APAR in plantations, providing insights for optimizing forest structure, enhancing forest quality, and implementing precise forest management practices, such as selective breeding for superior tree species.

A new method for detailed discharge and volume measurements of debris flows based on high-frequency 3D LiDAR point clouds; Illgraben, Switzerland

Debris flows are one of the most dangerous landslide types in mountainous regions. Their destructiveness is strongly controlled by their high peak discharge, which can be orders of magnitude larger than for floods. In order to reduce the associated hazards, detailed field measurements of natural debris flows are required to better constrain discharge and volume of these events. In this study, we used a high-frequency 3D LiDAR (light detection and ranging) scanner in combination with video cameras to measure key properties of a debris flow that occurred in the Illgraben catchment (Switzerland). Based on the LiDAR and video data, we directly measured i) front velocity ii) surge velocity iii) surface velocity and iv) cross-sectional area at sub-second intervals. We then estimated discharge and volume using these direct measurements and considering different channel bed geometry scenarios. We compared our results to estimates based on conventional methods and found that these more established methods substantially underestimate the (peak) discharge and volume for this event. Our results will be assessed in the future by analyzing more events, but our LiDAR-based method has the potential to provide much more detailed information on hazard-related debris-flow parameters, which will have important implications for understanding debris-flow processes and ultimately reducing their risk.

INVESTIGATING THE COMPLEMENTARY USE OF RADAR AND LIDAR FOR POSITIONING APPLICATIONS

Abstract. In the realm of Autonomous Vehicles (AVs), accurate, reliable and uninterrupted positioning capabilities are vital to ensure successful operations. Light Detection And Ranging (LiDAR) technology, capable of providing a high-fidelity 3D representation of the surrounding environment, has enabled numerous odometry-based positioning algorithms. These algorithms utilize a registration process to estimate relative motion from two successive 3D scans. However, the accuracy of the registration process can be compromised by the presence of dynamic objects, leading to significant translational and rotational deviations. On the other hand, Radar technology provides spatial and speed information. However, it is limited by spatial sparsity and susceptibility to noise. In this paper, we propose combining the complementary LiDAR and Electronic Scanning Radar (ESR) measurements, along with onboard motion sensors for improved navigation performance in complex and dynamic environments. This is achieved by employing a radar-based filtering mechanism that refines the LiDAR’s point cloud mitigating the impact of dynamic objects. This results in a more robust registration process, which in turn enhances the LiDAR Inertial Odometry (LIO) solution. The proposed method was verified using real data collected from onboard motion sensors, a 3D LiDAR, and four ESRs from road tests conducted in downtown Calgary, Alberta, Canada. Our approach achieved an improved average horizontal positioning and heading RMSE of 0.43 meters and 0.25 degrees, respectively, compared to the 0.66 meters and 0.39 degrees observed with the standalone LIO solution. Moreover, submeter-level and lane-level accuracies were enhanced to 95% and 100% of the time, respectively, up from 85.7% and 94.9%.

High‐resolution model reconstruction and bridge damage detection based on data fusion of unmanned aerial vehicles light detection and ranging data imagery

AbstractDamage detection is essential for the maintenance of transportation infrastructure that experiences high daily traffic levels in potentially extreme environments and changes in use patterns. However, traditional physical inspection is always labor‐intensive, subjective, and biased, lacking the objective perspective required for a comprehensive and reliable assessment. Recently, unmanned aerial vehicles (UAVs) combined with emerging high‐performance sensor(s) have been intensively researched. Here, we present an aerial bridge surface survey method that can be used to assess damage. Existing damage detection methods focus on single types of damage and are limited in locating global damage, whereas our method detects two types of damage on the surface and marks them in a panoramic image. The workflow involves three steps: data acquisition using a meticulous UAV flight strategy that covers the entire surface, data processing using image‐based and point‐cloud models after polynomial rectification, and data output (i.e., damage detected by the combined models). To verify the method, a field test detected damage to two real bridges. A UAV equipped with a camera and light detection and ranging (LiDAR) equipment was employed. Experiments demonstrate the effectiveness of the proposed method, which is capable of producing accurate outputs and detecting damage with an average position error of 13.37 mm and a relative size error of 25.25%. Owing to the data fusion model taking advantage of two‐dimensional (2D) images and 3D LiDAR data, it outputs a high‐resolution 3D model and avoids environmental disturbances. After decision‐making‐level damage fusion, all position and size properties of damage information are computed into a panoramic damage image. This panoramic image showing all detecting damage helps technicians perform maintenance; the image can be zoomed to focus on any issue individually.

Crack inspection in tunnel structures by fusing information from a 3D light detection and ranging and pan-tilt-zoom camera system

This study proposes a practical crack inspection method for tunnel structures by fusing measurements from two novel sensors: 3D light detection and ranging (LiDAR) and pan-tilt-zoom (PTZ) camera. The proposed method comprises three phases: prerequisite phase, real-time measurement phase, and inspection phase. First, the 3D LiDAR and the PTZ camera is combined to generate 3D point cloud data (PCD) with RGB data prior to extract the plane contained the crack. Second, the PTZ camera tracked the crack of interest at the pre-registered location. Specifically, several signal processing methods including the Hough transform and random sample consensus are addressed to effectively recognize the crack of interest. Then, the pan and tilt of the camera are controlled to track the crack of interest and record the crack at several magnifications of the zoom automatically because the optimal magnification value of the zoom is unknown in practical field applications. Third, crack propagation rate is estimated by calculating the length of the crack using information on the crack recorded in previous measurements. Cracks in a recorded image are extracted by addressing a multiscale multilevel mask deep convolutional neural network (MSML Mask DCNN) and pixel-based clustering method. The MSML Mask DCNN constructs distinct feature maps to effectively extract features of cracks and the pixel-based clustering method effectively eliminate cracks not of interest to accurately calculate the crack propagation rate for the crack of interest. Finally, the crack propagation rate was calculated through statistical analysis with several images at different zoom magnifications to secure high accuracy. Field experiments demonstrate that the proposed method is effective to inspect cracks. Systematic analysis on experiments also revealed that all methods deployed in the crack inspection enable ensures high accuracy and robustness in field applications. Consequently, the proposed crack inspection system and method provide a cost-effective and autonomous solution for crack inspection of tunnel structures.

Cascaded 2D Micromirror with Application to LiDAR.

This paper introduced a novel approach to enhance the vertical scanning angle of a large aperture 2D electromagnetic micromirror through the utilization of a cascaded torsional beam design. The primary objective was to increase the vertical scanning angle without compromising the robustness, which was achieved by optimizing the trade-off between the rotation angle and the first mode of resonant frequency. The cascaded design provides flexibility to either increase the outer frame's rotation angle without sacrificing torsional stiffness or enhance the torsion beam's stiffness while maintaining the same rotation angle, thus elevating the first-mode resonant frequency and overall robustness. The effectiveness of the cascaded design was demonstrated through a comparative study with a non-cascaded 2D micromirror possessing the same aperture size, torque, and mass moment of inertia. Theoretical analysis and finite-element simulation are employed to determine critical parameters such as the stiffness ratio between the cascaded torsion beams, and to predict improvements in the scanning angle and primary resonant frequency brought by the cascaded design. Prototypes of both cascaded and non-cascaded designs are fabricated using a flexible printed circuit board combined with Computer numerical control (CNC) machining of a Ti-alloy thin film, confirming the superior performance of the cascaded 2D micromirror. The cascaded design achieved vertical scanning angles up to 26% higher than the traditional design when both were actuated at close resonance frequencies. Additionally, the micromirror was successfully integrated into a 3D LiDAR system. The light detection and ranging (LiDAR) system was modelled in Zemax OpticStudio to find the optimized design and assembly positions.

Robust Localization for Underground Mining Vehicles: An Application in a Room and Pillar Mine.

Most autonomous navigation systems used in underground mining vehicles such as load-haul-dump (LHD) vehicles and trucks use 2D light detection and ranging (LIDAR) sensors and 2D representations/maps of the environment. In this article, we propose the use of 3D LIDARs and existing 3D simultaneous localization and mapping (SLAM) jointly with 2D mapping methods to produce or update 2D grid maps of underground tunnels that may have significant elevation changes. Existing mapping methods that only use 2D LIDARs are shown to fail to produce accurate 2D grid maps of the environment. These maps can be used for robust localization and navigation in different mine types (e.g., sublevel stoping, block/panel caving, room and pillar), using only 2D LIDAR sensors. The proposed methodology was tested in the Werra Potash Mine located at Philippsthal, Germany, under real operational conditions. The obtained results show that the enhanced 2D map-building method produces a superior mapping performance compared with a 2D map generated without the use of the 3D LIDAR-based mapping solution. The 2D map generated enables robust 2D localization, which was tested during the operation of an autonomous LHD, performing autonomous navigation and autonomous loading over extended periods of time.

Real‐time 3D multi‐pedestrian detection and tracking using 3D LiDAR point cloud for mobile robot

AbstractMobile robots are used in modern life; however, object recognition is still insufficient to realize robot navigation in crowded environments. Mobile robots must rapidly and accurately recognize the movements and shapes of pedestrians to navigate safely in pedestrian‐rich spaces. This study proposes real‐time, accurate, three‐dimensional (3D) multi‐pedestrian detection and tracking using a 3D light detection and ranging (LiDAR) point cloud in crowded environments. The pedestrian detection quickly segments a sparse 3D point cloud into individual pedestrians using a lightweight convolutional autoencoder and connected‐component algorithm. The multi‐pedestrian tracking identifies the same pedestrians considering motion and appearance cues in continuing frames. In addition, it estimates pedestrians' dynamic movements with various patterns by adaptively mixing heterogeneous motion models. We evaluate the computational speed and accuracy of each module using the KITTI dataset. We demonstrate that our integrated system, which rapidly and accurately recognizes pedestrian movement and appearance using a sparse 3D LiDAR, is applicable for robot navigation in crowded spaces.

VoxelScape: Large Scale Simulated 3D Point Cloud Dataset of Urban Traffic Environments

Having a profound understanding of the surrounding environment is considered one of the crucial tasks for the reliable operation of future self-driving cars. Light Detection and Ranging (LiDAR) sensor plays a critical role in achieving such understanding due to its capability to perceive the world in 3D. Similar to 2D perception tasks, current state-of-the-art methods in 3D perception tasks rely on deep neural networks (DNNs). However, the performance of 3D perception tasks, specially point-wise semantic segmentation, is not on par with their 2D counterparts. One of the main reasons is the lack of publicly available labelled 3D point cloud datasets (PCDs) from 3D LiDAR sensors. In this work, we are introducing the VoxelScape dataset, a large-scale simulated 3D PCD with 100K annotated point cloud scans. The annotations in the VoxelScape dataset include both point-wise semantic labels and 3D bounding box labels. Additionally, we used a number of baseline approaches to validate the transferability of VoxelScape to real 3D PCD for two challenging 3D perception tasks. The promising results have shown that training DNNs on VoxelScape boosted the performance of the 3D perception tasks on the real PCD. Furthermore, we are also releasing the proposed data generation pipeline for the research community to facilitate realistic simulation of 3D LiDAR point cloud data for different scenarios beyond those covered in our VoxelScape dataset. The VoxelScape dataset and the corresponding LiDAR simulation codes are publicly available at https://voxel-scape.github.io/dataset

Acquisition of high-resolution topographic information in forest environments using integrated UAV-LiDAR system: System development and field demonstration

High-resolution topographic information of landslide-prone areas plays an important role in accurate prediction and characterization of potential landslides and mitigation of landslides-associated hazards. This study presents an advanced geomorphological surveying system that integrates the light detection and ranging (LiDAR) with an unmanned aerial vehicle (UAV), a multi-rotor aerial vehicle in specific, for prediction, monitoring and forensic analysis of landslides, and for maintenance of debris-flow barriers. The test-flight over a vegetated area demonstrates that the integrated UAV-LiDAR system can provide high-resolution, three-dimensional (3D) LiDAR point clouds below canopy and vegetation in forest environments, overcoming the limitation of aerial photogrammetry and terrestrial LiDAR platforms. An algorithm is suggested to delineate the topographic information from the acquired 3D LiDAR point clouds, and the accuracy and performance of the developed UAV-LiDAR system are examined through field demonstration. Finally, two field demonstrations are presented: the forensic analysis of the recent Gokseong landslide event, and the sediment deposition monitoring for debris-flow barrier maintenance in South Korea. The developed surveying system is expected to contribute to geomorphological field surveys in vegetated, forest environments, particularly in a site that is not easily accessible.

A 256 × 256 LiDAR Imaging System Based on a 200 mW SPAD-Based SoC with Microlens Array and Lightweight RGB-Guided Depth Completion Neural Network.

Light detection and ranging (LiDAR) technology, a cutting-edge advancement in mobile applications, presents a myriad of compelling use cases, including enhancing low-light photography, capturing and sharing 3D images of fascinating objects, and elevating the overall augmented reality (AR) experience. However, its widespread adoption has been hindered by the prohibitive costs and substantial power consumption associated with its implementation in mobile devices. To surmount these obstacles, this paper proposes a low-power, low-cost, single-photon avalanche detector (SPAD)-based system-on-chip (SoC) which packages the microlens arrays (MLAs) and a lightweight RGB-guided sparse depth imaging completion neural network for 3D LiDAR imaging. The proposed SoC integrates an 8 × 8 SPAD macropixel array with time-to-digital converters (TDCs) and a charge pump, fabricated using a 180 nm bipolar-CMOS-DMOS (BCD) process. Initially, the primary function of this SoC was limited to serving as a ranging sensor. A random MLA-based homogenizing diffuser efficiently transforms Gaussian beams into flat-topped beams with a 45° field of view (FOV), enabling flash projection at the transmitter. To further enhance resolution and broaden application possibilities, a lightweight neural network employing RGB-guided sparse depth complementation is proposed, enabling a substantial expansion of image resolution from 8 × 8 to quarter video graphics array level (QVGA; 256 × 256). Experimental results demonstrate the effectiveness and stability of the hardware encompassing the SoC and optical system, as well as the lightweight features and accuracy of the algorithmic neural network. The state-of-the-art SoC-neural network solution offers a promising and inspiring foundation for developing consumer-level 3D imaging applications on mobile devices.

Research on orchard navigation method based on fusion of 3D SLAM and point cloud positioning.

Accurate navigation is crucial in the construction of intelligent orchards, and the need for vehicle navigation accuracy becomes even more important as production is refined. However, traditional navigation methods based on global navigation satellite system (GNSS) and 2D light detection and ranging (LiDAR) can be unreliable in complex scenarios with little sensory information due to tree canopy occlusion. To solve these issues, this paper proposes a 3D LiDAR-based navigation method for trellis orchards. With the use of 3D LiDAR with a 3D simultaneous localization and mapping (SLAM) algorithm, orchard point cloud information is collected and filtered using the Point Cloud Library (PCL) to extract trellis point clouds as matching targets. In terms of positioning, the real-time position is determined through a reliable method of fusing multiple sensors for positioning, which involves transforming the real-time kinematics (RTK) information into the initial position and doing a normal distribution transformation between the current frame point cloud and the scaffold reference point cloud to match the point cloud position. For path planning, the required vector map is manually planned in the orchard point cloud to specify the path of the roadway, and finally, navigation is achieved through pure path tracking. Field tests have shown that the accuracy of the normal distributions transform (NDT) SLAM method can reach 5cm in each rank with a coefficient of variation that is less than 2%. Additionally, the navigation system has a high positioning heading accuracy with a deviation within 1° and a standard deviation of less than 0.6° when moving along the path point cloud at a speed of 1.0m/s in a Y-trellis pear orchard. The lateral positioning deviation was also controlled within 5cm with a standard deviation of less than 2cm. This navigation system has a high level of accuracy and can be customized to specific tasks, making it widely applicable in trellis orchards with autonomous navigation pesticide sprayers.

Demonstration of localization for construction vehicles using 3D LiDARs installed in the field

AbstractLocalization methods for autonomous construction vehicles include real‐time kinematic positioning through a global navigation satellite system (GNSS) and a scan matching with a light detection and ranging (LiDAR) attached to mobility. However, these conventional methods have low estimation accuracy when the vehicle's surroundings have few features and the vehicle is in the no‐GNSS area. For the estimation in such areas, this paper proposes a localization method that can estimate by matching a 3D model of a construction vehicle with a point cloud obtained from 3D LiDARs installed in a work area. To realize the high‐accuracy and high‐speed processing of the localization, we propose remodeling using the predictive motion model (RM) algorithm to modify the 3D model in the registration process. In the experimental results on rough terrain, we confirmed that our method can estimate a vehicle's position and yaw angle with accuracies of 0.121 m and 0.016 rad, respectively. In addition, compared with the case without the RM algorithm, the construction vehicle's position and yaw angle accuracies improved up to 5 and 12 times, respectively.