Lidar Mapping Research Articles

LAND ARRANGEMENT PLANNING ON RECLAMATION LAND OF PT. MEGA MULTI ENERGY, NORTH BARITO, CENTRAL KALIMANTAN

PT. Mega Multi Energi (PT MME) operates in the coal mining industry which causes land changes, so planning for land reclamation is carried out. This research aims to determine the slope, landform management, and reclamation planning. Results of slope stability analysis to design single slope and overall slope dimensions. A detailed topographic survey has been carried out by PT. MME uses LiDAR mapping with a total work area of ± 5000 Ha. This technology uses the latest advanced LiDAR or Light Detection and Ranging sensor which is designed for activities in the form of topographic surveys and 3-dimensional imaging, operated by plotting millions of laser beam points on objects at one time and then modeling them in the form of a 3-dimensional point cloud. The rocks that make up the slopes in the mining area of PT. MME is dominated by claystone and sandstone. An individual slope design with a height of 10 meters and a slope of 60° shows a safety factor (FK) value of > 1.5. Landform regulation is intended to reduce the speed of runoff, erosion sedimentation, and landslides, the shape and design of the slope must be gentle. Implementation of the reclamation plan starts with land preparation, erosion and sedimentation control, topsoil management, and revegetation.

Methodology of real-time 3D point cloud mapping with UAV lidar

Accurate and timely availability of LiDAR data is vital in some cases. To facilitate monitoring of any environmental changes, LiDAR systems can be designed, and carried by UAV platforms that can take off without major preparation. In this study, the methodology of the real-time LiDAR mapping system was developed in the laboratory. The designed system shortens the target-based flight planning and post-flight data processing. In this system, the data is taken instantly and thus the change in the mapping area can be detected quickly. The simulation system, produce 3D point cloud, and data was stored in a database for later analysis. The 3D visualization of the data obtained from our developed UAV-LiDAR system was carried out with a platform-independent interface designed as web-based. The X3D file format used in the study to produce 3D point data provide an infrastructure for AI and ML-based systems in identifying urban objects in systems containing big data such as LiDAR.

Error Analysis-Based Map Compression for Efficient 3-D Lidar Localization

Large-scale 3D lidar maps are widely used in mobile robot localization because they can provide excellent constraints. However, the enormous number of point clouds imposes constraints on communication, storage, and computation, which brings a massive demand for localization-oriented point cloud map compression. This paper proposes an efficient localization-oriented 3D lidar map compression algorithm. First, we construct a multi-pose lidar sampling model based on feasible regions so that the compressed map includes observation data on multiple trajectories. Then, a localization error sensitivity analysis is introduced to score the map points, and their localization contribution is calculated according to the 6-DOF scores and observability of the map points. Finally, according to the localization contribution of map points, multi-resolution map compression units and a specific line-to-plane ratio are used to compress the map. We have conducted multiple sets of comparative experiments with our self-recorded multi-trajectory dataset to demonstrate the effectiveness and efficiency of our algorithm. Compared with different map compression algorithms, the final results show that when the compression ratio drops to 0.1%, although other algorithms fail, our algorithm can still provide high localization accuracy, which reaches map compression for efficient localization.

Multi-Robot SLAM Using Fast LiDAR Odometry and Mapping

This paper presents an approach to implem enting centralized multirobot simultaneous localization and mapping (MR-SLAM) in an unknown environment based on LiDAR sensors. The suggested implementation addresses two main challenges faced in MR-SLAM, particularly in real-time applications: computing complexity (solving the problem with minimum time and resources) and map merging (finding the alignment between the maps and merging maps by integrating information from the aligned maps into one map). The proposed approach integrates Fast LiDAR and Odometry Mapping (FLOAM), which reduces the computational complexity of localization and mapping for individual robots by adopting a non-iterative two-stage distortion compensation method. This, in turn, accelerates inputs for the map merging algorithm and expedites the creation of a comprehensive map. The map merging algorithm utilizes feature matching techniques, Singular Value Decomposition (SVD), and the Iterative Closest Point (ICP) algorithm to estimate the transformation between the maps. Subsequently, the algorithm employs a map-merging graph to estimate the global transformation. Our system has been designed to utilize two robots and has been evaluated on datasets and in a simulated environment using ROS and Gazebo. The system required less computing time to build the global map and achieved good estimation accuracy.

Analysis and application research of mobile robot navigation related technologies

With social progress and technological development, mobile robots have played an important role in industries, medical care, safety, home and other fields due to their advantages of saving labor costs, reducing personnel work intensity, and avoiding potential job hazards. Especially in indoor environments, such as industrial indoor operations, warehousing and logistics distribution, indoor safety patrols, and other application scenarios, mobile robots have highlighted inestimable application value. Intelligent mobile robot is an important tool in the field of service and automation, and robot navigation technology is an important technology for intelligent mobile robot to achieve self-positioning, robot mapping and path planning. This paper expounds three technologies of mobile robot navigation mapping: visual mapping and positioning, lidar mapping and positioning, and sensor fusion mapping and positioning. After that, the method of path planning of the mobile robot is analyzed. Finally, the application of intelligent mobile robots in factory automation and supermarket guidance is pointed out, and the development trend of intelligent mobile robots is prospected.

GNSS-Assisted LiDAR Odometry and Mapping for Urban Environment

GNSS-Assisted LiDAR Odometry and Mapping for Urban Environment

An intensity-enhanced LiDAR SLAM for unstructured environments

Traditional LiDAR simultaneous localization and mapping (SLAM) methods rely on geometric features such as lines and planes to estimate pose. However, in unstructured environments where geometric features are sparse or absent, point cloud registration may fail, resulting in decreased mapping and localization accuracy of the LiDAR SLAM system. To overcome this challenge, we propose a comprehensive LiDAR SLAM framework that leverages both geometric and intensity information, specifically tailored for unstructured environments. Firstly, we adaptively extract intensity features and construct intensity constraints based on degradation detection, and then propose a multi-resolution intensity map construction method. The experimental results show that our method achieves a 55% accuracy improvement over the pure geometric LiDAR SLAM system and exhibits superior anti-interference capability in urban corner scenarios. Compared with Intensity-SLAM, the advanced intensity-assisted LiDAR SLAM, our method achieves higher accuracy and efficiency.

WiCRF2: Multi-Weighted LiDAR Odometry and Mapping With Motion Observability Features

WiCRF2: Multi-Weighted LiDAR Odometry and Mapping With Motion Observability Features

Distance Transform Pooling Neural Network for LiDAR Depth Completion.

Recovering dense depth maps from sparse depth sensors, such as LiDAR, is a recently proposed task with many computer vision and robotics applications. Previous works have identified input sparsity as the key challenge of this task. To solve the sparsity challenge, we propose a recurrent distance transform pooling (DTP) module that aggregates multi-level nearby information prior to the backbone neural network. The intuition of this module is originated from the observation that most pixels within the receptive field of the network are zero. This indicates a deep and heavy network structure has to be used to enlarge the receptive field aiming at capturing enough useful information as most processed signals are uninformative zeros. Our recurrent DTP module can fill in empty pixels with the nearest value in a local patch and recurrently transform distance to reach farther nearest points. The output of the proposed DTP module is a collection of multi-level semi-dense depth maps from original sparse to almost full. Processing this collection of semi-dense depth maps alleviates the network from the input sparsity, which helps a lightweight simplified ResNet-18 with 1M parameters achieve state-of-the-art performance on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) depth completion benchmark with LiDAR only. Besides the sparsity, the input LiDAR map also contains some incorrect values due to the sensor error. Thus, we further enhance the DTP with an error correction (EC) module to avoid the spreading of the incorrect input values. At last, we discuss the benefit of only using LiDAR for nighttime driving and the potential extension of the proposed method for sensor fusion and the indoor scenario. The code has been released online at https://github.com/placeforyiming/DistanceTransform-DepthCompletion.

NF-Atlas: Multi-Volume Neural Feature Fields for Large Scale LiDAR Mapping

NF-Atlas: Multi-Volume Neural Feature Fields for Large Scale LiDAR Mapping

The prediction of uneven snowpack response to forest thinning informs forest restoration in the central Sierra Nevada

AbstractThe Sierra Nevada has experienced unprecedented wildfires and reduced snowmelt runoff in recent decades, due partially to anthropogenic climate change and over a century of fire suppression. To address these challenges, public land agencies are planning forest restoration treatments, which have the potential to both increase water availability and reduce the likelihood of uncontrollable wildfires. However, the impact of forest restoration on snowpack is site specific and not well understood across gradients of climate and topography. To improve our understanding of how forest restoration might impact snowpack across diverse conditions in the central Sierra Nevada, we run the high‐resolution (1 m) energy and mass balance Snow Physics and Lidar Mapping (SnowPALM) model across five 23–75 km2 subdomains in the region where forest thinning is planned or recently completed. We conduct two virtual thinning experiments by removing all trees shorter than 10 or 20 m tall and rerunning SnowPALM to calculate the change in meltwater input. Our results indicate heterogeneous responses to thinning due to differences in climate and wind across our five central Sierra Nevada subdomains. We also predict the largest increases in snow retention when thinning forests with tall (7–20 m) and dense (40–70% canopy cover) trees, highlighting the importance of pre‐thinning vegetation structure. We develop a decision support tool using a random forests model to determine which regions would most benefit from thinning. In many locations, we expect major forest restoration to increase snow accumulation, while other areas with short and sparse canopies, as well as sunny and windy climates, are more likely to see decreased snowpack following thinning. Our decision support tool provides stand‐scale (30 m) information to land managers across the central Sierra Nevada region to best take advantage of climate and existing forest structure to obtain the greatest snowpack benefits from forest restoration.

Architecture, wealth and status in Classic Maya urbanism revealed by airborne lidar mapping

Archaeologists have long associated high-quality Classic Maya (250-900 CE) masonry buildings with elite and perishable material structures with commoners. Currently, little agreement exists on how their distribution on the landscape may relate to models of socio-political organization in Maya urbanism due to the limited extent of settlement data in the highly forested Maya lowlands. Here we present a method for detecting masonry buildings on detailed lidar-derived maps at an unprecedented scale. We analyze the spatial distribution of 110,000 structures from different subregions of the Maya lowlands to explore patterns in Classic-period social and economic organization. Our results demonstrate that Maya elites consistently located themselves at regular intervals among the general population in urban and rural areas alike to optimally oversee the use and exchange of resources. This spatial arrangement likely favored flow of resources through every level of the social hierarchy and through different environmental zones to improve overall system efficiency and sustainability.

Quadric Representations for LiDAR Odometry, Mapping and Localization

Quadric Representations for LiDAR Odometry, Mapping and Localization

A LiDAR-intensity SLAM and loop closure detection method using an intensity cylindrical-projection shape context descriptor

LiDAR Simultaneous Localization and Mapping (SLAM) is able to map unknown scene while online estimating the LiDAR’s pose. Traditional LiDAR SLAM frameworks mainly rely on geometric features in the environments, but ignore LiDAR intensity information, leading to low accuracy in scenes with sparse environmental features. This paper proposes a novel intensity-based LiDAR-SLAM framework. In the front-end, the geometric and intensity features are extracted to match the two consecutive scans. To keep time efficiency, a self-adaptive feature selection strategy is proposed to select geometric and intensity features and the 6 DOF transformation from the corresponding features are weighted fused for odometry estimation. To improve the effectiveness of loop closure detection (LCD), we propose a novel intensity cylindrical-projection shape context (ICPSC) descriptor and a row-column similarity estimation based on ICPSC. To guarantee the accuracy of LCD, a double-value loop candidate verification strategy is employed. We have conducted comprehensive experimental verification of our LiDAR SLAM framework, including both the public KITTI datasets and data from our platform that involve multiple scenes. The results display that our framework achieves an averaged improvement of about 3 m in scenes with sparse environmental features in terms of on-line localization relative to LeGO-LOAM, while only increases maximum 9 ms in terms of time cost.

Efficient and Accurate Vehicle Localization Based on LiDAR Place Recognition

An efficient and accurate LiDAR place recognition methodology is proposed for vehicle localization. First, the Iris-LOAM is proposed to overcome the disadvantages of low accuracy of loop-closure detection and low efficiency of map construction in the existing LOAM-series methods. The method integrates the LiDAR-Iris global descriptor and Normal Distribution Transform (NDT) registration method into the loop-closure detection module of LiDAR Odometry and Mapping (LOAM), thereby improving the accuracy and efficiency of map construction. For the shortcomings of low map loading and matching efficiency, the Random Sample Consensus method is used to remove the ground point cloud information. The Voxel Grid method is used to down sample the loaded map. Finally, the NDT method is adopted for point cloud map matching to obtain the position information. Show that the Iris-LOAM has higher efficiency than the SC-LeGO-LOAM. The average time of point cloud map matching is reduced by 39.5%. The place recognition can be executed to achieve accuracy vehicle localization.

SLAM-based Navigation Technology for Rescue Robots in Post-disaster Scenarios

In order to solve the problems of difficulty and high-risk factor in implementing rescue after disasters, this paper designs an intelligent rescue robot autonomous navigation system based on LIDAR synchronous positioning and map building, with a view to achieving autonomous navigation of robots in complex post-disaster scenarios to complete rescue tasks. Firstly, the autonomous navigation system senses the scene by LiDAR and uses gmapping algorithms to construct a map of the post-disaster environment. Secondly, adaptive Monte Carlo localization algorithm is used to achieve robot localization based on radar and odometer data. Then the robot rescue work path is planned to use the Dijkstra algorithm. And TEB local planning path algorithm is used to control the robot. Finally, to verify the reliability of the autonomous navigation system designed in this paper, the ROS system software framework is used as the basis. The SLAM map construction, global path planning, and local real-time obstacle avoidance are tested practically under the scenario to ensure that the autonomous navigation of the mobile robot meets the requirements.

ASL-SLAM: A LiDAR SLAM With Activity Semantics-Based Loop Closure

A practical back-end module with loop closure detection is very useful and important for a LiDAR simultaneous localization and mapping (SLAM) system to perform high-precision positioning and mapping tasks. However, most existing loop closure detection methods are based on images or point clouds, and these methods may produce errors when the structure or texture is similar. To overcome this problem, we propose a complete LiDAR SLAM system, including a front-end odometry module based on normal distribution transform (NDT)-LOAM and a back-end optimization module with loop closure based on activity semantics. Through the analysis and calculation of inertial measurement unit (IMU) data from SLAM platforms such as unmanned ground vehicles (UGVs), the activity semantics of turning and passing over a speed bump are detected based on the peak <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${z}$ </tex-math></inline-formula> -axis angular velocity and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${z}$ </tex-math></inline-formula> -axis acceleration, respectively. Then, according to this activity semantics information and its unique and definite attributes, we establish correct loop closure detection using rough geometric detection, activity semantics matching, and point cloud rematching for validation. Finally, graph optimization theory is utilized to reduce the global cumulative error, improve the global trajectory accuracy and map consistency, and obtain the final global motion trajectory and point cloud map. We collected a dataset for evaluation, which contains indoor data, outdoor data, and indoor–outdoor integration data, and we also evaluated our method on the KITTI dataset. The experimental results for different scenes show that the addition of activity semantics can effectively help loop closure detection and improve LiDAR SLAM system performance.

Dual LIDAR online calibration and mapping and perception system

Making autonomous mobile devices capable of autonomous positioning and map building in a GPS-denied environment as well as being able to circumvent people in the operating area simultaneously, is essential for many UGV appliances. In this paper, a mapping and positioning system using dual LIDAR is proposed for positioning and building maps. The system may also detect people in the operating space of the equipment using infrared heat maps. The localization and mapping information from each LIDAR is unified in a loosely coupled approach after simultaneous extrinsic calibration. We propose a method for constructing factor graph using LIDAR point cloud geometric features to optimally solve the dual LIDAR extrinsic in real time. We tested the localization accuracy with a publicly available dataset and three real-world scenarios and compared it with three existing methods. The test results in open-source datasets and real scenarios show that our proposed method improves the position and attitude estimation by up to 50% compared to the three existing methods, and the proposed dual LIDAR extrinsic calibration method can achieve an estimation accuracy of 0.05 m for the translational extrinsic and 1deg for the rotational extrinsic. In addition, our proposed method achieves well position and attitude estimation when other existing methods show severe drift in trajectory estimation in real scenarios. Thus, our proposed method is suitable for obtaining high accuracy measurements of LIDAR extrinsic parameters and for dense mapping and accurate localization in the environment of GNSS-denied and human mobility.

Research on positioning and mapping algorithm of sliding window optimization for substation monitoring robot

This paper proposed a positioning and mapping algorithm of sliding window optimization for substation monitoring robots to solve the problems of low precision location and poor robustness of the existing laser odometer in power inspection outdoor scene mapping. A tightly coupled simultaneous localization and mapping (SLAM) algorithm was proposed based on 16-wire LiDAR and inertial measurement unit (IMU). Firstly, the paper estimated the IMU and corrected the motion distortion of the laser point cloud by linear interpolation. Secondly, scene features were extracted by curvature and classified according to different feature properties. The local map was constructed in the sliding window using the inter-frame matching module. Finally, the joint optimization function was built using the distance and IMU data obtained by matching the frame with the local map. The paper used the KITTI and self-recorded datasets to conduct the experiments. The results show that the improved method’s accuracy outperforms the lightweight and ground-optimized LiDAR odometry and Mapping (Lego-LOAM) and LiDAR inertial odometry and mapping (LIO-Mapping) and draws a broad application prospect in power inspection field.

Versatile LiDAR-Inertial Odometry With SE(2) Constraints for Ground Vehicles

LiDAR SLAM has become one of the major localization systems for ground vehicles since LiDAR Odometry And Mapping (LOAM). Many extension works on LOAM mainly leverage one specific constraint to improve the performance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , information from on-board sensors such as loop closure and inertial state; prior conditions such as ground level and motion dynamics. In many robotic applications, these conditions are often known partially, hence a SLAM system can be a comprehensive problem due to the existence of numerous constraints. Therefore, we can achieve a better SLAM result by fusing them properly. In this paper, we propose a hybrid LiDAR-inertial SLAM framework that leverages both the on-board perception system and prior information such as motion dynamics to improve localization performance. In particular, we consider the case for ground vehicles, which are commonly used for autonomous driving and warehouse logistics. We present a computationally efficient LiDAR-inertial odometry method that directly parameterizes ground vehicle poses on SE(2). The out-of-SE(2) motion perturbations are not neglected but incorporated into an integrated noise term of a novel SE(2)-constraints model. For odometric measurement processing, we propose a versatile, tightly coupled LiDAR-inertial odometry to achieve better pose estimation than traditional LiDAR odometry. Thorough experiments are performed to evaluate our proposed method's performance in different scenarios, including localization for both indoor and outdoor environments. The proposed method achieves superior performance in accuracy and robustness.