Light Detection And Ranging Sensors Research Articles

Water level identification with laser sensors, inertial units, and machine learning

Flood risk management usually hinges on accurate water level identification in urban streams such as rivers or creeks. Although research has emphasised the applicability of ultrasonic sensors as a contactless technology for sensor-based water level monitoring, Light Detection and Ranging (LiDAR) sensors are less sensitive to weather conditions that typically happen during flood events, such as dust, fog and rainfall. However, there has been little research on the applicability of LiDAR sensors in this field. No previous literature has analysed the impact of complicating variables on the quality of predictions or evaluated the possible benefits of using a combined approach with Inertial Measurement Units (IMU) and machine learning to produce superior predictions. In this work, we collected a dataset in a laboratory condition synchronising data from a LiDAR, an ultrasonic sensor and an IMU in an experimental device. We controlled the incidence angle, the distance, and the water turbidity to analyse their effect on the predictions. Traditional machine-learning techniques were evaluated as models to combine data from distance and inertial sensors, reducing the error rates compared to individual sensors’ predictions. Results indicated a sharp drop in the mean absolute error, root mean squared error and coefficient of determination for all water turbidity and incidence angles considered, especially when tree-based ensembles were used. The ultrasonic sensor led to improved results for low water turbidity and increased incidence angle, but statistically significant differences were not found in the other cases.

Enhancing Accessibility: Assistive Devices Pairing Mobile Apps for Visually Impaired People

Vision impairment and blindness are significant health conditions that affect a considerable portion of the global population and have significant consequences, particularly in developed nations. Its harmful consequences can have a negative influence on a person's personal quality of life, as well as limit their ability to live independently. Navigation plays a crucial role in the activities that are impacted by vision loss. This is because it is almost impossible for the individual to move safely and independently. The goal of this project is to improve mobility for visually impaired people by employing Light Detection and Ranging (LiDAR) sensors to identify obstacles and alert users via haptic feedback if there are potential obstacles in their way. In addition, this study compares two sensors, LiDAR and ultrasonic sensors, besides the implementation of the Internet of Things (IoT), which was integrated into the small, portable robot by pairing a mobile phone. This is to ensure the user may be securely guided to their destination. I-Walk is a mobile app with a voice user interface (VUI) for GPS navigation systems and emergency calling. This app has been developed to allow users safe and independent travel. The simulation result shows that even though ultrasonic sensors are cheaper and serve the same function as LiDAR, their huge standard deviation and percentage error shown in this study cannot equal LiDAR's accuracy in which LiDAR has an average error of 0.63%. In contrast, ultrasonic sensors have an average error of 2.39%. Overall, this obstacle detection and navigation system comprises robust hardware and software to assist visually impaired people effectively.Keywords: Assistive technology, blind, mini guider robot, obstacle detection, navigation, IoT

EMOS: Enhanced moving object detection and classification via sensor fusion and noise filtering

AbstractDynamic object detection is essential for ensuring safe and reliable autonomous driving. Recently, light detection and ranging (LiDAR)‐based object detection has been introduced and shown excellent performance on various benchmarks. Although LiDAR sensors have excellent accuracy in estimating distance, they lack texture or color information and have a lower resolution than conventional cameras. In addition, performance degradation occurs when a LiDAR‐based object detection model is applied to different driving environments or when sensors from different LiDAR manufacturers are utilized owing to the domain gap phenomenon. To address these issues, a sensor‐fusion‐based object detection and classification method is proposed. The proposed method operates in real time, making it suitable for integration into autonomous vehicles. It performs well on our custom dataset and on publicly available datasets, demonstrating its effectiveness in real‐world road environments. In addition, we will make available a novel three‐dimensional moving object detection dataset called ETRI 3D MOD.

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.

Multispectral LiDAR-based underwater ore classification using a tunable laser source

Light Detection and Ranging (LiDAR) technology has been extensively used to collect precise geometric information. However, conventional LiDAR sensors are restricted to single-wavelength operation, limiting their ability to capture valuable spectral information. Multispectral LiDAR (MSL) systems, on the other hand, have emerged as a promising solution by facilitating the active acquisition of spatial and spectral data. These advanced systems exhibit tremendous potential in facilitating advanced analysis and classification of seafloor sediments. In this study, the first demonstration of an MSL system optimized for investigating the feasibility of underwater ore classification using a tunable laser source is described. The MSL prototype features a spectral resolution of 10 nm, and 11 spectral channels, covering the range from 460 to 560 nm. Laboratory-based experiments were conducted to evaluate the accuracy of range measurements and the classification performance of the system. The spectral profiles of eight distinct ore samples acquired by the MSL were utilized for classification using four classification methods: naive Bayes (NB), k Nearest Neighbor (KNN), support vector machine (SVM), and random forest (RF). Furthermore, comparative analyses were conducted to investigate the classification enhancements realized by leveraging the MSL system with multiple spectral channels as opposed to single-wavelength and dual-wavelength systems.

High-accurate vehicle trajectory extraction and denoising from roadside LIDAR sensors

In recent years, Light Detection and Ranging (LIDAR) has been widely used in the Intelligent Traffic System due to its high-precision data acquisition capabilities. It has great potential for accurately detecting and tracking vehicle trajectories on the roadside. This study aims to develop a novel methodological framework for accurate vehicle trajectory extraction from roadside LIDAR. The proposed method starts by designing a detection area, followed by the application of statistical filtering, density clustering, and a random sample consensus (RANSAC) segmentation algorithm to eliminate the background point cloud. Subsequently, a modified density-based spatial clustering of applications with a noise algorithm (BSO-DBSCAN) is employed to detect the vehicle based on a beetle swarm optimization algorithm. An oriented bounding box (OBB) is used to obtain vehicle position, length, and width. A modified trajectory association method based on the Kalman filtering algorithm is proposed to address vehicle occlusion. LIDAR is deployed to obtain vehicle operation data in the expressway work zone, and the performance of the proposed method is tested using an unmanned aerial vehicle (UAV). The experimental results demonstrate that the proposed method successfully extracts high-precision vehicle trajectories. Specifically, the vehicle recognition accuracy increased by 12.7% in MAPE and 14.9% in RMSE compared to DBSCAN. The trajectory tracking accuracy increased by 13.5%, and the number of ID switching (ID-SW) was reduced by 271 times compared to SORT. The vehicle trajectory data extracted in this study provides a foundation for traffic characteristic analysis and traffic modeling. The extracted data can be downloaded from the following GitHub: https://github.com/gao0628/Dataset.

Study on self-diagnosis/prediction technology for LIDAR sensor of autonomous vehicles

Along with the development of autonomous driving technology, the need for self-failure diagnosis and Remaining Useful Life (RUL) prediction technology for core parts for autonomous driving is increasing. In particular, the characteristics of the light detection and ranging (LIDAR) sensor exposed to the outside further increase the need to apply fault diagnosis and RUL prediction technology considering various environmental variables. In this study, based on the accelerated degradation test of LIDAR, the failure mode was analyzed. Through this, LIDAR failure due to thermal runaway, which is the first failure type in high temperature conditions, was identified, and whether there were major environmental data that could identify thermal runaway was identified. In the case of LIDAR's thermal runaway phenomenon, a study on an algorithm to identify the precursor symptoms of failure in an accidental failure situation is conducted. Afterwards, through the actual vehicle test process, various environmental variable information is analyzed for correlation with LIDAR internal sensor data, and the abnormal data for the temperature of the internal parts of the LIDAR is predicted through the external environmental sensor.

Development of vehicle- recognition method on water surfaces using LiDAR data: SPD2 (spherically stratified point projection with diameter and distance)

Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces. For monitoring natural environments and conducting security activities within a certain range using a surface vehicle, the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time. It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles. For this purpose, a LiDAR (light detection and ranging) sensor is used as it can simultaneously obtain 3D information for all directions, relatively robustly and accurately, irrespective of the surrounding environmental conditions. Although the GPS (global-positioning-system) error range exists, obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering. In this study, a three-layer convolutional neural network is applied to classify types of surface vehicles. The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes. Hence, we have proposed a descriptor that converts the 3D point cloud data into 2D image data. To use this descriptor effectively, it is necessary to perform a clustering operation that separates the point clouds for each object. We developed voxel-based clustering for the point cloud clustering. Furthermore, using the descriptor, 3D point cloud data can be converted into a 2D feature image, and the converted 2D image is provided as an input value to the network. We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator. Furthermore, we explore the feasibility of real-time object classification within this framework.

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

Chances and Challenges: Transformation from a Laser-Based to a Camera-Based Container Crane Automation System

In recent years, a steady increase in maritime business and annual container throughput has been recorded. To meet this growing demand, terminal operators worldwide are turning to automated container handling. For the automated operation of a crane, a reliable capture of the environment is required. In current state-of-the-art applications this is mostly achieved with light detection and ranging (LiDAR) sensors. These sensors enable precise three-dimensional sampling of the surroundings, even at great distances. However, the use of LiDAR sensors has a number of disadvantages, such as high acquisition costs and limited mounting positions. This raises the question of whether the LiDAR systems of automated container terminals (ACT) can be replaced with cameras. However, this transformation is not easy to accomplish and is explored in more depth in this paper. The field of camera-based container automation presented in this publication is largely unexplored. To the best of our knowledge, there is currently no automated container terminal in real-world operation that exclusively uses cameras. This publication aims to create a basis for further scientific research towards the goal of a fully camera-based container automation. Therefore, the authors present a narrative review providing a broad overview of the mentioned transformation, identifying research gaps, and suggesting areas for future research. In order to achieve this, this publication examines the fundamentals of an automated container terminal, the existing automation solutions and sensor technologies, as well as the opportunities and challenges of a transformation from LiDAR to camera.

Stockpile Volume Estimation in Open and Confined Environments: A Review

This paper offers a comprehensive review of traditional and advanced stockpile volume-estimation techniques employed within both outdoor and indoor confined spaces, whether that be a terrestrial- or an aerial-based technique. Traditional methods, such as manual measurement and satellite imagery, exhibit limitations in handling irregular or constantly changing stockpiles. On the other hand, more advanced techniques, such as global navigation satellite system (GNSS), terrestrial laser scanning (TLS), drone photogrammetry, and airborne light detection and ranging (LiDAR), have emerged to address these challenges, providing enhanced accuracy and efficiency. Terrestrial techniques relying on GNSS, TLS, and LiDAR offer accurate solutions; however, to minimize or eliminate occlusions, surveyors must access geometrically constrained places, representing a serious safety hazard. With the speedy rise of drone technologies, it was not unexpected that they found their way to the stockpile volume-estimation application, offering advantages such as ease of use, speed, safety, occlusion elimination, and acceptable accuracy compared to current standard methods, such as TLS and GNSS. For outdoor drone missions, image-based approaches, like drone photogrammetry, surpass airborne LiDAR in cost-effectiveness, ease of deployment, and color information, whereas airborne LiDAR becomes advantageous when mapping complex terrain with vegetation cover, mapping during low-light or dusty conditions, and/or detecting small or narrow objects. Indoor missions, on the other hand, face challenges such as low lighting, obstacles, dust, and limited space. For such applications, most studies applied LiDAR sensors mounted on tripods or integrated on rail platforms, whereas very few utilized drone solutions. In fact, the choice of the most suitable technique/approach depends on factors such as site complexity, required accuracy, project cost, and safety considerations. However, this review puts more focus on the potential of drones for stockpile volume estimation in confined spaces, and explores emerging technologies, such as solid-state LiDAR and indoor localization systems, which hold significant promise for the future. Notably, further research and real-world applications of these technologies will be essential for realizing their full potential and overcoming the challenges of operating robots in confined spaces.

A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors.

In this work, we introduce a novel approach to model the rain and fog effect on the light detection and ranging (LiDAR) sensor performance for the simulation-based testing of LiDAR systems. The proposed methodology allows for the simulation of the rain and fog effect using the rigorous applications of the Mie scattering theory on the time domain for transient and point cloud levels for spatial analyses. The time domain analysis permits us to benchmark the virtual LiDAR signal attenuation and signal-to-noise ratio (SNR) caused by rain and fog droplets. In addition, the detection rate (DR), false detection rate (FDR), and distance error derror of the virtual LiDAR sensor due to rain and fog droplets are evaluated on the point cloud level. The mean absolute percentage error (MAPE) is used to quantify the simulation and real measurement results on the time domain and point cloud levels for the rain and fog droplets. The results of the simulation and real measurements match well on the time domain and point cloud levels if the simulated and real rain distributions are the same. The real and virtual LiDAR sensor performance degrades more under the influence of fog droplets than in rain.

Real-Time Adaptive Data Transmission Against Various Traffic Load in Multi-LIDAR Sensor Network for Indoor Monitoring

One of the key components in smart cities is smart monitoring, a process for collecting information to efficiently manage the infrastructures and resources and enables citizens to engage in intelligent activities. For both indoor and outdoor monitoring, light detection and ranging (LIDAR) technology has been used to collect three-dimensional data useful for smart monitoring. Blind spots can be effectively covered by aggregating point clouds from multiple LIDAR sensors (sensors hereafter) with different viewpoints. Although multiple-LIDAR (multi-LIDAR) sensor networks have been investigated, prior studies have focused on monitoring outdoor scenes with a relatively low number of sensors, mostly for road safety. For indoor use, however, ten or more sensors are required to eliminate blind spots, as the placement of sensors is limited by ceilings. When the network bandwidth is limited because of other traffic loads, the transmission size needs to be small while including the highly important points. Therefore, adaptive control is required for sensor networks. We propose a system that adaptively controls the data volume of a multi-LIDAR sensor network considering the current network state in real time. It consists of a single edge computer and multiple sensors. While aggregating data streams, the edge computer monitors the current delay status for each sensor and sends notifications to change the data volume by the sensor.We report on the results of two experimental evaluations conducted in different environments: one to validate the effectiveness of the proposed system and the other to verify its practicality.

Technical Note: Validation of Recon-3D, iPhone LiDAR for bullet trajectory documentation

Bullet trajectory documentation is an important part of shooting reconstructions. Manual methods are quite common, using a protractor and angle gauge, but more advanced methods using laser scanners and photogrammetry are also used for documentation. Past studies have shown that using terrestrial laser scanners (TLS) can reduce the variability in trajectory documentation. In 2020, Apple Inc. released the iPad Pro & iPhone 12 Pro with a Light Detection and Ranging (LiDAR) sensor, which effectively placed a low-cost laser scanner in mobile devices. In May of 2022, an iOS application focused on forensics called Recon-3D was released. Recon-3D uses Apple’s LiDAR sensor and fuses it with photogrammetry to produce 3D point cloud data in the e57 format. The purpose of this research was to compare the accuracy, repeatability, and quality of 3D data, using Recon-3D to the Faro Focus S350 laser scanner in bullet trajectory documentation. As a first test, twelve trajectory rods were installed on a wooden panel in a controlled, indoor environment, in order to optimize the best settings for scanning trajectory rods. Subsequently, a more practical exercise was run outdoors with a vehicle containing five trajectory rods. The mean horizontal angle error for the twelve trajectory rods installed on the wooden panel was 0.14° with a standard deviation of 0.30°. The mean error for the vertical angle was − 0.05° with a standard deviation of 0.25°. For the outdoor vehicle test, the mean error for the horizontal angle was 0.03° with a standard deviation of 0.28°. The mean error for the vertical angle was 0.22° with a standard deviation of 0.36°. These errors are similar to other studies which utilize the terrestrial laser scanner with mean errors well below 1°. Although further work is required to make Recon-3D a more robust application, preliminary results are promising and in line with previous studies where the terrestrial laser scanner has been used. Thus, Recon-3D appears suitable to document trajectory rods when used in shooting reconstructions.

A Study on the Effectiveness of SCD Seeding Fog Dissipation Mechanism Using LiDAR Sensor

Fog interferes with traffic flow and causes major accidents. In foggy conditions, traffic accident death rates are higher than in other weather conditions. Research on fog dissipation technology is needed to reduce the incidence of accidents caused by fog. There are various artificial methods to remove fog. In this study, two methods of natural dissipation by gravity sedimentation and a solid carbon dioxide seeding fog dissipation mechanism were compared and analyzed in cold fog conditions. Solid carbon dioxide was selected as the fog dissipation particle. In this experiment, solid carbon dioxide seeding with three different values of weight (500 g, 1000 g, and 1500 g) was considered. This is because fog particles can be supercooled and fog can be removed. A light detection and ranging (LiDAR) sensor were used to quantitatively check the effect of improving visibility when solid carbon dioxide was seeded in the fog. The LiDAR sensor detects the surrounding environment through distance measurements by emitting lasers and processing the laser responses. A camera was used to visually observe the phenomenon occurring inside the calorimetric chamber. As a result, the fog dissipation mechanism using solid carbon dioxide seeding under cold fog conditions was proven to be effective in improving the visible distance compared with natural dissipation.

Reconstruction methods for the mechanical energy of a tree under free vibration

This study examines the mechanical energy comprising the sum of kinetic energy and potential energy for improving understanding of tree dynamics under free vibration. Two methods are proposed to reconstruct the mechanical energy by means of trees’ motion and dynamic properties measured by a multi-beam flash light detection and ranging (LiDAR) sensor. The first method reconstructs the mechanical energy using the dynamic properties extracted from stochastic subspace identification. The second method uses a model updating technique that optimizes a finite-element model by minimizing an objective function, which quantifies the differences between the results obtained from the finite-element model and that measured by the LiDAR sensor, via a particle swarm optimization algorithm. The proposed methods are applied to analyze the vibration of a Ficus microcarpa under pull-and-release tests. In general, both methods yield consistent results in reconstructing the mechanical energy, kinetic energy, and potential energy of the whole tree. Results reveal that the tree tends to use more vibration modes at the beginning than at the end of vibration. The energy distribution among branches and trunk is further analyzed. The results show that the kinetic energy of the branches could exceed that of the trunk. In addition, the rate of energy dissipation is positively related to the velocity at the pulling location, and hence, the tree tends to dissipate more mechanical energy at the beginning than at the end of free vibration.

Collaborative positioning for swarms: A brief survey of vision, LiDAR and wireless sensors based methods

As positioning sensors, edge computation power, and communication technologies continue to develop, a moving agent can now sense its surroundings and communicate with other agents. By receiving spatial information from both its environment and other agents, an agent can use various methods and sensor types to localize itself. With its high flexibility and robustness, collaborative positioning has become a widely used method in both military and civilian applications. This paper introduces the basic fundamental concepts and applications of collaborative positioning, and reviews recent progress in the field based on camera, LiDAR (Light Detection and Ranging), wireless sensor, and their integration. The paper compares the current methods with respect to their sensor type, summarizes their main paradigms, and analyzes their evaluation experiments. Finally, the paper discusses the main challenges and open issues that require further research.

Review of Scanning and Pixel Array-Based LiDAR Point-Cloud Measurement Techniques to Capture 3D Shape or Motion

Developments in light detection and ranging (LiDAR) technology have brought innovations in three-dimensional (3D) measurement. After mechanical laser scanners were introduced in the 1990s, the speed and point density of LiDAR measurements have increased considerably with the developments in photon imagers. On the other hand, lightweight and small-size LiDAR sensors and their integrated use with other related sensors have made the use of LiDAR widespread for mapping and navigation purposes on mobile platforms. Matrix imaging LiDAR cameras and solid-state laser scanners have no or fewer moving parts for measurement, and are unaffected by vibrations. They are usually used in mobile mapping, driverless vehicle navigation, and mobile robot navigation. Pulse or phase-shift methods are used to measure the distance from the LiDAR instrument to the scan point. The measured scan point direction is determined by the orientation angles of the beam in scanners, focal length, and pixel positions in matrix viewers, and instrument-centered 3D coordinates are calculated. LiDAR tools have their own specific capabilities and limitations. Therefore, the selection of the appropriate LiDAR for any application is very important. In this study, after LiDAR principles are introduced, scanning LiDAR and pixel-based matrix imager LiDAR methods used to measure 3D point clouds are technically examined and analyzed.

Efficient Detection and Tracking of Human Using 3D LiDAR Sensor

Light Detection and Ranging (LiDAR) technology is now becoming the main tool in many applications such as autonomous driving and human–robot collaboration. Point-cloud-based 3D object detection is becoming popular and widely accepted in the industry and everyday life due to its effectiveness for cameras in challenging environments. In this paper, we present a modular approach to detect, track and classify persons using a 3D LiDAR sensor. It combines multiple principles: a robust implementation for object segmentation, a classifier with local geometric descriptors, and a tracking solution. Moreover, we achieve a real-time solution in a low-performance machine by reducing the number of points to be processed by obtaining and predicting regions of interest via movement detection and motion prediction without any previous knowledge of the environment. Furthermore, our prototype is able to successfully detect and track persons consistently even in challenging cases due to limitations on the sensor field of view or extreme pose changes such as crouching, jumping, and stretching. Lastly, the proposed solution is tested and evaluated in multiple real 3D LiDAR sensor recordings taken in an indoor environment. The results show great potential, with particularly high confidence in positive classifications of the human body as compared to state-of-the-art approaches.

AFLI-Calib: Robust LiDAR-IMU extrinsic self-calibration based on adaptive frame length LiDAR odometry

As an effective complement to common laser scanning systems, the portable laser scanning system can acquire point clouds flexibly and quickly. Calibration between Light detection and ranging (LiDAR) sensors and inertial measurement units (IMU) is the prerequisite for laser scanning systems to obtain high-quality point clouds. Related methods have been proposed in the last two decades, where the global navigation satellite system (GNSS) or high-precision calibration fields are commonly used. However, the extrinsic self-calibration of LiDAR-IMU is challenging, due to the large distortion in single-frame point cloud caused by rapid motion and the position errors of IMU integration which drift quickly. At the same time, the highly dynamic motion patterns of portable devices and the changes in the scanned scene structure are not well considered in existing LiDAR odometry methods. To take better advantage of the characteristics of non-repetitive scanning LiDAR sensor, this paper proposes AFLI-Calib, which utilizes adaptive frame length LiDAR odometry to perform the extrinsic self-calibration of LiDAR-IMU. Unlike LiDAR odometry methods with a fixed frame length, the LiDAR frame length is dynamically adjusted according to the motion state of sensors and the matching stability of scenes. The single-frame point cloud is registered to the map through a linear-based continuous-time model, eliminating the motion distortion correction in advance. For further optimization of trajectory and extrinsic parameters, IMU raw measurements and LiDAR observations are involved in the multi-constraint optimization, through tightly-coupled IMU pre-integration constraints, LiDAR point-to-plane constraints, and prior constraints. The method is fully validated using self-collected calibration data of indoor and outdoor scenes and different motion modes. Experiments show that on the test data, the translation parameter accuracy of the method is 0.041 m, which is 56.3% higher than the state-of-the-art method. The standard deviation is significantly reduced, with translation deviation (0.017 m, 0.024 m, 0.022 m) and rotation deviation (0.17°, 0.25°, 0.15°), which verifies the robustness of our method. The average RMSE of distances to the reference point cloud acquired by the terrestrial laser scanning system (TLS) is 0.042 m, showing a high accuracy calibration result. Comparative experiments with the fixed frame length LiDAR odometry method and classical “correction-then-registration” motion distortion model further verify the superiority and effectiveness of the proposed adaptive frame length LiDAR odometry.