3D LiDAR Sensor Research Articles

Real-Time Semantic Segmentation of 3D LiDAR Point Clouds for Aircraft Engine Detection in Autonomous Jetbridge Operations

This paper presents a study on aircraft engine identification using real-time 3D LiDAR point cloud segmentation technology, a key element for the development of automated docking systems in airport boarding facilities, known as jetbridges. To achieve this, 3D LiDAR sensors utilizing a spinning method were employed to gather surrounding environmental 3D point cloud data. The raw 3D environmental data were then filtered using the 3D RANSAC technique, excluding ground data and irrelevant apron areas. Segmentation was subsequently conducted based on the filtered data, focusing on aircraft sections. For the segmented aircraft engine parts, the centroid of the grouped data was computed to determine the 3D position of the aircraft engine. Additionally, PointNet was applied to identify aircraft engines from the segmented data. Dynamic tests were conducted in various weather and environmental conditions, evaluating the detection performance across different jetbridge movement speeds and object-to-object distances. The study achieved a mean intersection over union (mIoU) of 81.25% in detecting aircraft engines, despite experiencing challenging conditions such as low-frequency vibrations and changes in the field of view during jetbridge maneuvers. This research provides a strong foundation for enhancing the robustness of jetbridge autonomous docking systems by reducing the sensor noise and distortion in real-time applications. Our future research will focus on optimizing sensor configurations, especially in environments where sea fog, snow, and rain are frequent, by combining RGB image data with 3D LiDAR information. The ultimate goal is to further improve the system’s reliability and efficiency, not only in jetbridge operations but also in broader autonomous vehicle and robotics applications, where precision and reliability are critical. The methodologies and findings of this study hold the potential to significantly advance the development of autonomous technologies across various industrial sectors.

Unveiling gap acceptance behaviour during lane change with EDIV data: A deep dive into driving behaviour on expressway using a three level mixed effect linear regression approach

Lane change has a potential significance in road safety. Gap acceptance phenomena serves as a primary and critical phase in lane change maneuver. This study aims to investigate the gap acceptance behaviour of drivers during lane changes on expressways, with a focus on understanding how various factors influence drivers' decisions to change lanes. An extensive dataset collected through various sensors tailored for expressway driving, known as the ‘Expressway Drive: Instrumented Vehicle (EDIV) Dataset’ is utilized. Driving data from 59 drivers covering a distance of around 4000 km was used in the current study. Total 2578 lane changing events are identified through computing lateral deviations measured through 3D LiDAR sensor. Substantial differences are observed within the groups in primary analysis which suggest that lane-change direction significantly affect gap acceptance. To effectively manage both intra- and inter-cluster variances, this study employs two separate three levels mixed-effects linear models. These models account for the interdependence of gap acceptance characteristics within individual drivers and for different directions of lane changes by incorporating random effects. Furthermore, these models examine relationships between lead/ lag gap acceptance and the various influencing factors as fixed effects. It was found that factors such as speed of the subject vehicle, gap position, relative speeds, and surrounding vehicle types had influence on gap acceptance during lane changes on expressways. The insights gained from this study could inform the development of advanced driver assistance systems (ADAS) as well as development of autonomous vehicles, contributing to improved road safety and traffic flow management in high-speed environments.

Semantic enrichment of BIM with IndoorGML for quadruped robot navigation and automated 3D scanning

Planning scan routes with prior knowledge can improve scan data quality and completeness. This paper presents a BIM-enabled approach to optimize quadruped robot navigation for automated 3D scanning. The BIM data schema is enriched with IndoorGML, integrating building geometry with spatial data to establish an indoor navigation model describing multi-scale spatial topological networks. This navigation model, which includes an enhanced greedy algorithm, optimizes quadruped robot scanning positions and traversal sequences. The scan planning optimization outperforms existing heuristic algorithms in computational efficiency, coverage, and scan point count. The BIM-enabled approach is validated on ROS and in real-world conditions with a 3D LiDAR sensor integrated with a quadruped robot. The robotic scans achieve visible coverage of 70–90% of the structure, with a fluctuation of 0.006–0.021 mm compared to traditional laser scans. The findings demonstrate robotic scans as a viable way of obtaining complete and accurate point clouds, reducing human effort in traditional scanning.

Research on the drone endoscopic system for fusion reactors

This paper proposes an air-ground separation drone endoscopic system (DES) design scheme for the fusion reactor internal inspection task. A foldable rotor drone with vertical takeoff and landing capability is used as the main body of DES. The nest platform is designed separately from the drone to reduce the weight. Meanwhile, the nest platform can be installed with a safety cable and 2D code to realize emergency rescue and precise take-off and landing. DES adds 3D lidar sensors for 360° stereo sensing, realizing real-time positioning in dark or even lightless environments in vacuum chambers. The first-generation DES prototype is developed, and the flight test is performed inside the PF6 superconducting coil shell by fusing 3D lidar point cloud data with visual camera image data. The results show that the position error of DES is stabilized within ±0.05 m during the hovering phase. The error is mainly due to the narrow space inside the PF6 coil shell, which produces a perturbed flow field on the DES. In general, the DES accomplished circumferential autonomous flight control and 3D point cloud data acquisition in a confined space, proving the feasibility of drone inspection in vacuum chambers.

ANN-Based LiDAR Positioning System for B5G.

This work reports the development of an efficient and precise indoor positioning system utilizing two-dimensional (2D) light detection and ranging (LiDAR) technology, aiming to address the challenging sensing and positioning requirements of the beyond fifth-generation (B5G) mobile networks. The core of this work is the implementation of a 2D-LiDAR system enhanced by an artificial neural network (ANN), chosen due to its robustness against electromagnetic interference and higher accuracy over traditional radiofrequency signal-based methods. The proposed system uses 2D-LiDAR sensors for data acquisition and digital filters for signal improvement. Moreover, a camera and an image-processing algorithm are used to automate the labeling of samples that will be used to train the ANN by means of indicating the regions where the pedestrians are positioned. This accurate positioning information is essential for the optimization of B5G network operation, including the control of antenna arrays and reconfigurable intelligent surfaces (RIS). The experimental validation demonstrates the efficiency of mapping pedestrian locations with a precision of up to 98.787%, accuracy of 95.25%, recall of 98.537%, and an F1 score of 98.571%. These results show that the proposed system has the potential to solve the problem of sensing and positioning in indoor environments with high reliability and accuracy.

Sparsity-Robust Feature Fusion for Vulnerable Road-User Detection with 4D Radar

Detecting vulnerable road users is a major challenge for autonomous vehicles due to their small size. Various sensor modalities have been investigated, including mono or stereo cameras and 3D LiDAR sensors, which are limited by environmental conditions and hardware costs. Radar sensors are a low-cost and robust option, with high-resolution 4D radar sensors being suitable for advanced detection tasks. However, they involve challenges such as few and irregularly distributed measurement points and disturbing artifacts. Learning-based approaches utilizing pillar-based networks show potential in overcoming these challenges. However, the severe sparsity of radar data makes detecting small objects with only a few points difficult. We extend a pillar network with our novel Sparsity-Robust Feature Fusion (SRFF) neck, which combines high- and low-level multi-resolution features through a lightweight attention mechanism. While low-level features aid in better localization, high-level features allow for better classification. As sparse input data are propagated through a network, the increasing effective receptive field leads to feature maps of different sparsities. The combination of features with different sparsities improves the robustness of the network for classes with few points.

Developing a Flying Explorer for Autonomous Digital Modelling in Wild Unknowns

Digital modelling stands as a pivotal step in the realm of Digital Twinning. The future trend of Digital Twinning involves automated exploration and environmental modelling in complex scenes. In our study, we propose an innovative solution for robot odometry, path planning, and exploration in unknown outdoor environments, with a focus on Digital modelling. The approach uses a minimum cost formulation with pseudo-randomly generated objectives, integrating multi-path planning and evaluation, with emphasis on full coverage of unknown maps based on feasible boundaries of interest. The approach allows for dynamic changes to expected targets and behaviours. The evaluation is conducted on a robotic platform with a lightweight 3D LiDAR sensor model. The robustness of different types of odometry is compared, and the impact of parameters on motion planning is explored. The consistency and efficiency of exploring completely unknown areas are assessed in both indoor and outdoor scenarios. The experiment shows that the method proposed in this article can complete autonomous exploration and environmental modelling tasks in complex indoor and outdoor scenes. Finally, the study concludes by summarizing the reasons for exploration failures and outlining future focuses in this domain.

SyS3DS: Systematic Sampling of Large-Scale LiDAR Point Clouds for Semantic Segmentation in Forestry Robotics

Recently, new semantic segmentation and object detection methods have been proposed for the direct processing of three-dimensional (3D) LiDAR sensor point clouds. LiDAR can produce highly accurate and detailed 3D maps of natural and man-made environments and is used for sensing in many contexts due to its ability to capture more information, its robustness to dynamic changes in the environment compared to an RGB camera, and its cost, which has decreased in recent years and which is an important factor for many application scenarios. The challenge with high-resolution 3D LiDAR sensors is that they can output large amounts of 3D data with up to a few million points per second, which is difficult to process in real time when applying complex algorithms and models for efficient semantic segmentation. Most existing approaches are either only suitable for relatively small point clouds or rely on computationally intensive sampling techniques to reduce their size. As a result, most of these methods do not work in real time in realistic field robotics application scenarios, making them unsuitable for practical applications. Systematic point selection is a possible solution to reduce the amount of data to be processed. Although our approach is memory and computationally efficient, it selects only a small subset of points, which may result in important features being missed. To address this problem, our proposed systematic sampling method called SyS3DS (Systematic Sampling for 3D Semantic Segmentation) incorporates a technique in which the local neighbours of each point are retained to preserve geometric details. SyS3DS is based on the graph colouring algorithm and ensures that the selected points are non-adjacent in order to obtain a subset of points that are representative of the 3D points in the scene. To take advantage of the ensemble learning method, we pass a different subset of nodes for each epoch. This leverages a new technique called auto-ensemble, where ensemble learning is proposed as a collection of different learning models instead of tuning different hyperparameters individually during training and validation. SyS3DS has been shown to process up to 1 million points in a single pass. It outperforms the state of the art in efficient semantic segmentation on large datasets such as Semantic3D. We also present a preliminary study on the validity of the performance of LiDAR-only data, i.e., intensity values from LiDAR sensors without RGB values for semi-autonomous robot perception.

An autonomous Unmanned Aerial Vehicle exploration platform with a hierarchical control method for post‐disaster infrastructures

AbstractCatastrophic natural disasters like earthquakes can cause infrastructure damage. Emergency response agencies need to assess damage precisely while repeating this process for infrastructures with different shapes and types. The authors aim for an autonomous Unmanned Aerial Vehicle (UAV) platform equipped with a 3D LiDAR sensor to comprehensively and accurately scan the infrastructure and map it with a predefined resolution r. During the inspection, the UAV needs to decide on the Next Best View (NBV) position to maximize the gathered information while avoiding collision at high speed. The authors propose solving this problem by implementing a hierarchical closed‐loop control system consisting of a global planner and a local planner. The global NBV planner decides the general UAV direction based on a history of measurements from the LiDAR sensor, and the local planner considers the UAV dynamics and enables the UAV to fly at high speed with the latest LiDAR measurements. The proposed system is validated through the Regional Scale Autonomous Swarm Damage Assessment simulator, which is built by the authors. Through extensive testing in three unique and highly constrained infrastructure environments, the autonomous UAV inspection system successfully explored and mapped the infrastructures, demonstrating its versatility and applicability across various shapes of infrastructure.

Automated rerailing of a road-rail shunting vehicle on road-level tracks using 2D-Lidar

Road-rail vehicles are emerging increasingly in small flat yards for shunting wagons. While numerous research works suggest automation of the shunting operation to improve its efficiency, the rerailing procedure of such vehicle, i.e., switching between road and rail mode, remains manually executed. Hence, we present a simple but novel method for a road-rail vehicle with skid-steer drive to rerail automatically on road-level tracks (e.g., level-crossing). The vehicle uses two 2D-Lidar sensors to scan the floor for (rail) in order to estimate its lateral displacement and angular orientation towards the track centre. A simplified model and a PID-type controller for the yaw rate were deployed while keeping the linear velocity constant for the vehicle to align itself at the centre of and parallel to the track. Upon reaching the goal, the rail wheels are descended and the rerailing is completed. Test runs were conducted from a random initial pose near the track centre while limiting the travel range in x-direction to 2.5 m, yet allowing a change of direction each time the vehicle reaches this limit. In 97% of the test runs, the vehicle rerailed successfully in 47 s on average (12 s of which was needed for lowering the rail wheels) with three changes of direction or less. In the remaining 3% of cases it took more attempts. Automated rerailing could be used not only as assistance function for the shunting personnel, but also as important feature of an autonomous shunting robot that is able to access both road and rail domains.

Extrinsic Calibration of Thermal Camera and 3D LiDAR Sensor via Human Matching in Both Modalities during Sensor Setup Movement.

LiDAR sensors, pivotal in various fields like agriculture and robotics for tasks such as 3D object detection and map creation, are increasingly coupled with thermal cameras to harness heat information. This combination proves particularly effective in adverse conditions like darkness and rain. Ensuring seamless fusion between the sensors necessitates precise extrinsic calibration. Our innovative calibration method leverages human presence during sensor setup movements, eliminating the reliance on dedicated calibration targets. It optimizes extrinsic parameters by employing a novel evolutionary algorithm on a specifically designed loss function that measures human alignment across modalities. Our approach showcases a notable 4.43% improvement in the loss over extrinsic parameters obtained from target-based calibration in the FieldSAFE dataset. This advancement reduces costs related to target creation, saves time in diverse pose collection, mitigates repetitive calibration efforts amid sensor drift or setting changes, and broadens accessibility by obviating the need for specific targets. The adaptability of our method in various environments, like urban streets or expansive farm fields, stems from leveraging the ubiquitous presence of humans. Our method presents an efficient, cost-effective, and readily applicable means of extrinsic calibration, enhancing sensor fusion capabilities in the critical fields reliant on precise and robust data acquisition.

3D far-field Lidar sensing and computational modeling for human identification.

3D sensors offer depth sensing that may be used for task-specific data processing and computational modeling. Many existing methods for human identification using 3D depth sensors primarily focus on Kinect data, where the range is very limited. This work considers a 3D long-range Lidar sensor for far-field imaging of human subjects in 3D Lidar full motion video (FMV) of "walking" action. 3D Lidar FMV data for human subjects are used to develop computational modeling for automated human silhouette and skeleton extraction followed by subject identification. We propose a matrix completion algorithm to handle missing data in 3D FMV due to self-occlusion and occlusion from other subjects for 3D skeleton extraction. We further study the effect of noise in the 3D low resolution far-field Lidar data in human silhouette extraction performance of the model. Moreover, this work addresses challenges associated with far-field 3D Lidar including learning with a limited amount of data and low resolution. Moreover, we evaluate the proposed computational algorithm using a gallery of 10 subjects for human identification and show that our method is competitive with the state-of-the-art OpenPose and V2VPose skeleton extraction models using the same dataset for human identification.

Critical voxel learning with vision transformer and derivation of logical AV safety assessment scenarios

Safety assessment is an active research subject for autonomous vehicles (AVs) that have emerged as a new mode of mobility. In particular, scenario-based safety assessments have garnered significant attention. AVs can be tested on how they safely avoid hypothetical situations leading to accidents. However, scenarios written by humans based on their expert knowledge and experience may only partially reflect real-world situations. Instead, we are keen on a different technique of extracting statistically significant and more detailed scenarios from sensor data captured during the critical moments when AVs become vulnerable to potential accidents. Specifically, we first render the three-dimensional space around an AV with fixed-sized voxels. Then, we modeled the aggregate kinetics of the objects in each voxel detected by 3D-LiDAR sensors mounted on real test AVs. The Vision Transformer we used to model the kinetics helped us quickly pinpoint critical voxels containing objects that threatened the AV’s safety. We traced the trajectory of the critical voxels on a visual attention map to describe in detail how AVs become vulnerable to accidents according to the logical scenario format defined by the PEGASUS Project. We tested our novel method with 250 h of 3D-LiDAR recordings capturing critical moments. We devised an inference model that detected critical situations with an F1-score of 98.26%. For each type of scenario, our model consistently identified the critical objects and their tendency to influence AVs. Given the evaluation results, we can ensure that our data-driven approach yields an AV safety assessment scenario with high representativeness, coverage, expansion, and computational feasibility.

LSMCL: Long-term Static Mapping and Cloning Localization for autonomous robot navigation using 3D LiDAR in dynamic environments

One of the challenges in autonomous robot navigation applications is recognizing the exact location in a dynamic environment in which the location of surrounding objects changes frequently. This study proposes a long-term static mapping and cloning localization (LSMCL) method for estimating real-time accurate location using only natural landmarks even in a dynamic environment using a 3D LiDAR sensor. LSMCL comprises long-term static mapping (LSM) and cloning localization (CL). A LSM creates a 2D grid map and a 3D geometric feature map for objects whose positions do not change in space. A CL uses the generated 2D grid map and particle filter to estimate the 2D global position at the initial stage. After cloning the 2D global location to 3D space, the location is tracked through a 3D feature map and map matching. To verify the LSMCL’s usability in a real dynamic environment, a robot navigation experiment was conducted in a highly dynamic parking lot. The experimental results, analyzed in terms of initial localization success rate, location estimation accuracy and precision, processing time, and congested space application confirmed the LSMCL’s real-world applicability.

3D lidar SLAM-based systems in object detection and navigation applications

ABSTRACT This paper considered an object detection system based on 3D LiDAR Sensor and Simultaneous Localization and Mapping (SLAM) to complete the navigation applications of mobile robots. A 3D-based SLAM with lightweight and ground-optimized Lidar odometry and mapping (LeGO-LOAM) appropriately generated the environmental maps. SLAM is a tool used to obtain information from the environment, allowing mobile robots to know their location. Indoor environment data is immedicably created while SLAM is processing the information. The dynamic object detection algorithm depends on the available information to realize the external morphology and circle the bounding box of moving objects. Therefore, a wheeled mobile robot (WMR) was employed to dynamically trace the object’s movement direction. Finally, This study found that the quantum genetic algorithm (QGA) is more efficient in generating a shorter path than the particle swarm optimization, and a dynamic window approach (DWA) is immediately detected as a dynamic obstacle. Therefore, WMR obtains enough object, obstacle, and routing information to effectively and safely reach the destination through the Move_base software package in Robot Operating System.

Hybrid Navigation of an Autonomous Mobile Robot to Depress an Elevator Button

The development of an autonomous mobile robot (AMR) with an eye-in-hand robot arm atop for depressing elevator button is proposed. The AMR can construct map and perform localization using the ORB-SLAM algorithm (the Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features) feature detector-Simultaneous Localization and Mapping). It is also capable of real-time obstacle avoidance using information from 2D-LiDAR sensors. The AMR, robot manipulator, cameras, and sensors are all integrated under robot operating system (ROS). In experimental investigation to dispatch the AMR for depressing an elevator button, AMR navigation initiating from the laboratory is divided into three parts. First, the AMR initiated navigation using ORB-SLAM for most of the journey to a waypoint nearby the elevator. The resulting mean absolute error (MAE) is 8.5 cm on the x-axis, 10.8 cm on the y-axis, 9.2-degree rotation angle about the z-axis, and the linear displacement from the reference point is 15.1 cm. Next, the ORB-SLAM is replaced by an odometry based 2D-SLAM method for further navigating the AMR from waypoint to a point facing the elevator between 1.5 to 3 meter distance, where the ORB-SLAM is ineffective due to sparse feature points for localization and where the elevator can be clearly detected by an eye-in-hand machine vision onboard the AMR. Finally, the machine vision identifies space position of the elevator and again the odometry based 2D SLAM method is employed for navigating the AMR to front of the elevator between 0.3 to 0.5 meter distance. Only at this stage, the small-size elevator button can be detected and reached by the robot arm on the AMR. An averaged 60% successful rate of button depressing by the AMR starting at the laboratory is obtained in the experiments. Improvements of successful elevator button depressing rate are also pointed out.

LiDAR‐based object detection for agricultural robots

AbstractLight detection and ranging (LiDAR) sensors have proven to be a valuable tool to gather spatial information about the environment and are a crucial component in perception of autonomous systems. In the agricultural domain, state‐of‐the‐art algorithms for detection, classification, and tracking often utilize a combination of LiDAR and camera by fusing both semantic and spatial information. This is in part due to the availability of fast algorithms specifically optimized to make use of the convenient 2D data structure of RGB images. Still, there are limitations relying on cameras in agriculture because they highly depend on favorable lighting conditions and generally lack explicit geometric information, whereas LiDAR is especially advantageous in that regard as well as regarding range. This makes LiDAR particularly valuable for perception applications such as self‐localization, mapping, or object detection. The unstructured nature of 3D LiDAR point clouds, however, coupled with their possibly large size and density, presents a significant hurdle in terms of real‐time capability for these kinds of tasks. Object detection on 3D LiDAR sensor data is therefore challenging and solutions in agriculture usually are tied to very narrow use cases or rigorous down sampling to ensure real‐time applicability. Here, we present an algorithm featuring 2.5D map representations to avoid the computational drawbacks and ensure real‐time capability. We utilize established algorithms to project the 3D LiDAR data into two distinct maps and then apply object detection algorithms on each of those maps individually and to subsequently combine the information into a joint estimate. From the information stored in the 2.5D map, axis‐aligned bounding boxes for each object are computed containing information about position and dimensions. We present a proof of concept and assess the real‐time capability for a domain‐specific use case.

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

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

High-Resolution Mobile Mapping Platform Using 15-mm Accuracy LiDAR and SPAN/TerraStar C-PRO Technologies

Nowadays, most of the mobile mapping systems use GNSS/INS positioning technology and two-dimensional sensors to construct maps, self-localize, and gather environmental information, as well. Several problems can arise with traditional architectures of these systems, especially in situations where the GNSS signal is unavailable or multiple paths are involved, such as reliability issues and poor accuracy. Moreover, their cost of up to 2 million USD still poses a significant challenge for the development of new GIS applications. This paper proposes a new design of a mobile mapping system that incorporates a 1.5 cm accurate 3D LiDAR sensor and a high accuracy positioning system based on SPAN/TerraStar C-PRO technologies. The Extended Kalman Filter was used in this research to reduce the impact of GNSS signal loss by combining the SLAM method with SPAN/TerraStar C-PRO technologies. In the experiments, the concept of our mobile mapping platform was validated using the simulation environment Gazebo. So as to evaluate the proposed platform, a real dataset was collected from a complex environment where the GNSS signal is rarely available, exactly, from the campus of Moncton -Université de Moncton. The obtained results disclosed that the proposed platform proves its performance in terms of accuracy and reliability. Due to the integration of SLAM algorithm with SPAN/TerraStarC-PRO technologies, the generated 3D point cloud map includes a number of 285 million points with an mean accuracy 0.28 m even in the case of GNSS signal loss.

Ploughing furrow recognition for onland ploughing using a 3D-LiDAR sensor

The purpose of the work presented in this paper is to develop a robust method of autonomous vehicle localisation during soil tilling, using a 3D LiDAR sensor. The approach was developed for onland ploughing but is transferable to other applications with similar features. To detect a ploughing furrow from multiple layers in a 3D point cloud, a recognition method was employed consisting of two parallel and redundant branches: (1) detecting the furrow edge in each layer by identifying the characteristic step, and (2) approximating the unploughed surface by applying Euclidean clustering and linear least-squares line fitting. The redundant branches were then fused into a guidance directrix by RANSAC line fitting. To evaluate the algorithm, human-labelled reference points of the furrow were obtained by elaborate manual labelling of the LiDAR ploughing data. The results of the recognition method were compared with the reference points, resulting in a minimum of 86 % identical recognised points, with a mean error of 6.5 mm.