Common LiDAR (Light Detection and Ranging) Terminology

• 3D modeling: The process of using LiDAR data to create a digital representation of an object or environment in three dimensions.
• Accuracy: The degree to which the measured distance or position of an object agrees with its true distance or position.
• Aerial LiDAR: LiDAR mounted on aircraft or drones for mapping from above.
• Bullet Point List All LiDAR Terminology and Related Definitions.
• Field of view (FOV): The range of angles over which the LiDAR sensor can detect returns.
• LiDAR (Light Detection and Ranging): A remote sensing technology that uses laser pulses to measure the distance to a target and create highly accurate 3D models of the surrounding environment.
• LiDAR Accuracy : The degree to which the measured distance or position of an object agrees with its true distance or position. This can be affected by factors such as the power of the laser, the sensitivity of the sensor, and the environment in which the LiDAR is being used.
• LiDAR Field of View (FOV): The range of angles over which the LiDAR sensor can detect returns. This can be limited by the design of the sensor and the optics used.
• LiDAR Frame: A collection of laser pulses and returns captured by the LiDAR sensor at a specific point in time.
• LiDAR Point Cloud : A point cloud is a set of data points representing a 3D object or surface, created by LiDAR. Each data point in the point cloud has a set of coordinates (x, y, z) that represents its position in 3D space.
• LiDAR Point Cloud Animation: The process of creating animations or videos from LiDAR data, typically by rendering the point cloud or a 3D model derived from it in real-time or offline.
• LiDAR Point Cloud Annotation: The process of adding semantic information, such as labels or attributes, to the points in a point cloud. This can be used to train machine learning models or to create 3D maps.
• LiDAR Point Cloud Automated feature extraction: The process of using algorithms to extract features such as edges, corners, surfaces, and objects from the point cloud data. This can be used to improve the accuracy and efficiency of feature detection.
• LiDAR Point Cloud Classification: The process of assigning semantic labels to points in a point cloud, such as “building” or “tree.” This can be done using techniques such as supervised learning or semantic segmentation.
• LiDAR Point Cloud Compression: The process of reducing the size of a LiDAR point cloud by removing redundant information or encoding the data in a more efficient format. This can be done using techniques such as lossless compression, lossy compression, or progressive compression.
• LiDAR Point Cloud Data Accuracy : The degree of agreement between the coordinates of the points in a LiDAR point cloud and their true positions in the real world. It can be affected by factors such as sensor calibration, atmospheric conditions, and terrain.
• LiDAR Point Cloud Data API: An Application Programming Interface (API) that allows developers to access and manipulate LiDAR point cloud data using programming languages such as Python, C++, or Java.
• LiDAR Point Cloud Data Augmented Reality (AR) : The process of superimposing virtual objects on the real world using LiDAR point cloud data. This can be used for navigation, training, and visualization.
• LiDAR Point Cloud Data Classification: The process of assigning semantic labels to points in a point cloud, such as “building” or “tree.” This can be done using techniques such as supervised learning or semantic segmentation.
• LiDAR Point Cloud Data Cleansing: The process of removing noise and outliers from the point cloud data. This can be done using various techniques such as statistical filtering, ground classification, and noise reduction.
• LiDAR Point Cloud Data Cloud-based Platform: A cloud-based platform that provides storage, processing, and analytics capabilities for LiDAR point cloud data. This can include services such as data management, data visualization, and data processing.
• LiDAR Point Cloud Data Completeness : The degree to which a LiDAR point cloud covers an area of interest. It can also measure how well the point cloud captures the geometry and texture of the objects and surfaces in the scene.
• LiDAR Point Cloud Data Compression : The process of reducing the size of a LiDAR point cloud by removing redundant information or encoding the data in a more efficient format. This can be done using techniques such as lossless compression, lossy compression, or progressive compression.
• LiDAR Point Cloud Data Consistency : The degree of agreement between different parts of a LiDAR point cloud, such as between multiple returns from the same location or between point clouds captured at different times or from different viewpoints.
• LiDAR Point Cloud Data Density : The number of points per unit area in a LiDAR point cloud. It’s a measure of the resolution of the data and is typically measured in points per square meter.
• LiDAR Point Cloud Data Filtering: The process of removing unwanted data from the point cloud data, such as outliers or errors. This can be done using various techniques such as statistical filtering, ground classification, and noise reduction.
• LiDAR Point Cloud Data Format: The file format in which the LiDAR point cloud data is stored. Common formats include LAS, LAZ, PLY, PCD, and XYZ.
• LiDAR Point Cloud Data Fusion: The process of combining data from multiple sources, such as LiDAR, cameras, and GPS, to create a more complete and accurate representation of the environment.
• LiDAR Point Cloud Data Georeferencing: The process of assigning geographic coordinates to the points in a point cloud. This can be done using techniques such as GPS, ground control points, or aerial imagery.
• LiDAR Point Cloud Data Library: A collection of pre-built software modules that can be used to perform common tasks such as point cloud visualization, registration, or feature extraction.
• LiDAR Point Cloud Data Mining: The process of extracting useful information from the point cloud data using various techniques such as pattern recognition, cluster analysis, and decision trees.
• LiDAR Point Cloud Data Orthorectification: The process of adjusting the point cloud data to correct for distortions caused by sensor orientation, terrain, and atmospheric effects.
• LiDAR Point Cloud Data Processing Algorithm : A set of instructions that can be executed by a computer to perform a specific task on LiDAR point cloud data, such as filtering, registration, segmentation, or classification.
• LiDAR Point Cloud Data Processing Library : A collection of pre-built software modules that can be used to perform common tasks such as point cloud visualization, registration, or feature extraction.
• LiDAR Point Cloud Data Processing Pipeline: A series of interconnected processing steps that are applied to the LiDAR point cloud data, such as filtering, registration, segmentation, and classification, to extract meaningful information.
• LiDAR Point Cloud Data Processing Software : A set of software tools that can be used to process and analyze LiDAR point cloud data, such as data visualization, filtering, registration, and classification.
• LiDAR Point Cloud Data Processing Toolbox : A collection of tools and utilities that can be used to process and analyze LiDAR point cloud data, such as data visualization, filtering, or registration.
• LiDAR Point Cloud Data Processing Workflow : A series of steps that are followed when processing LiDAR point cloud data, such as data acquisition, quality control, data cleaning, data registration, data processing and data visualization.
• LiDAR Point Cloud Data Quality Control: The process of evaluating the quality of the LiDAR data by comparing it to other sources of information or to established standards. This can include checking for errors, outliers, or inconsistencies in the data, and can be done using various tools and metrics.
• LiDAR Point Cloud Data Quality Metrics: A set of quantitative measures that can be used to evaluate the quality of LiDAR point cloud data. These can include measures such as point density, completeness, accuracy, and consistency.
• LiDAR Point Cloud Data Registration: The process of aligning multiple point clouds, typically captured from different viewpoints or at different times, into a common coordinate system. This can be done using techniques such as Iterative Closest Point (ICP) or Feature-Based Registration.
• LiDAR Point Cloud Data SDK: A Software Development Kit (SDK) that provides developers with the tools and libraries needed to develop applications that use LiDAR point cloud data.
• LiDAR Point Cloud Data Segmentation: The process of grouping points in a point cloud into meaningful clusters, such as individual objects or surfaces. This can be done using techniques such as region growing, clustering, or machine learning.
• LiDAR Point Cloud Data Semantic Annotation: The process of adding semantic information, such as labels or attributes, to the points in a point cloud. This can be used to train machine learning models or to create 3D maps.
• LiDAR Point Cloud Data Streaming: The process of transmitting LiDAR point cloud data in real-time over a network connection. This can be used for remote sensing, teleoperation, or real-time visualization.
• LiDAR Point Cloud Data Toolbox: A collection of tools and utilities that can be used to process and analyze LiDAR point cloud data, such as data visualization, filtering, or registration.
• LiDAR Point Cloud Data Virtual Reality (VR) : The process of creating a virtual 3D environment from LiDAR point cloud data. This can be used for navigation, training, and visualization.
• LiDAR Point Cloud Databases: The process of storing and managing LiDAR point cloud data in a structured and efficient way, typically using a database management system (DBMS) or a cloud-based storage platform.
• LiDAR Point Cloud Denoising: The process of removing noise from the point cloud data. This can be done using various techniques such as statistical filtering, smoothing, and filtering.
• LiDAR Point Cloud Processing: The process of analyzing and interpreting the point cloud data generated by the LiDAR sensor. This can include creating 3D models, extracting features such as edges and surfaces, and identifying objects and patterns in the data.
• LiDAR Point Cloud Quality assessment: The process of evaluating the quality of the point cloud data using various metrics such as density, completeness, accuracy and consistency.
• LiDAR Point Cloud Quality Assurance: The process of evaluating and ensuring the quality of LiDAR data, typically by comparing it to other sources of information or to established standards. This can include checking for errors, outliers, or inconsistencies in the data, and can be done using various tools and metrics.
• LiDAR Point Cloud Registration: The process of aligning multiple point clouds, typically captured from different viewpoints or at different times, into a common coordinate system. This can be done using techniques such as Iterative Closest Point (ICP) or Feature-Based Registration.
• LiDAR Point Cloud Segmentation: The process of grouping points in a point cloud into meaningful clusters, such as individual objects or surfaces. This can be done using techniques such as region growing, clustering, or machine learning.
• LiDAR Point Cloud Simplification: The process of reducing the number of points in a point cloud while preserving the overall shape and features of the data. This can be done using techniques such as decimation, voxelization, or octree-based methods.
• LiDAR Point Cloud Texturing: The process of adding color information to a point cloud, typically by projecting images captured by a camera onto the 3D points. This can help to improve the visual quality of the point cloud and make it easier to interpret.
• LiDAR Point Cloud Visualization: The process of displaying LiDAR data in a way that is easy to understand and interpret. This can include creating 2D or 3D plots, color-coding the data, or using interactive tools such as sliders or buttons to control the display.
• LiDAR Pulse: A single laser emission from the LiDAR sensor.
• LiDAR Range: The maximum distance at which the LiDAR sensor can detect returns. This can be limited by the power of the laser and the sensitivity of the sensor.
• LiDAR Resolution: The density of points in the point cloud, typically measured in points per square meter. This is determined by the number of laser pulses emitted and the density of the returns detected by the sensor.
• LiDAR Return : LiDAR return is the reflection of the laser pulse back to the sensor. LiDAR systems can detect multiple returns from a single pulse, allowing them to capture the shape and texture of objects and surfaces.
• LiDAR Scan Pattern: The pattern in which the LiDAR sensor emits laser pulses and receives returns. This can include the angle of the laser beam, the density of the pulses, and the speed of the sensor.
• LiDAR Sensor Calibration : The process of adjusting the LiDAR sensor’s parameters to ensure that it is measuring distances accurately. This can include adjusting the laser’s power, the sensor’s field of view, and the processor’s algorithms.
• LiDAR Sensor Fusion: The process of combining data from multiple sensors, such as LiDAR, radar, cameras, and GPS, to provide a more complete and accurate understanding of the environment.
• LiDAR System: A LiDAR system consists of a laser, a sensor, and a processor. The laser emits pulses of light, the sensor detects the reflected light, and the processor uses the time delay between the emitted and detected light to calculate the distance to the target.
• LiDAR Terminology Definitions
• Light Detection and Ranging (LiDAR) : LiDAR is a remote sensing technology that uses laser pulses to measure the distance to a target and create highly accurate 3D models of the surrounding environment.
• Mobile LiDAR: LiDAR mounted on vehicles for mapping while in motion.
• Obstacle Detection and Avoidance (ODA) use case: LiDAR sensor used in autonomous vehicles to detect and avoid obstacles.
• Point cloud: A set of data points representing a 3D object or surface, created by LiDAR.
• Pulse: A single laser emission from the LiDAR sensor.
• Range: The maximum distance at which the LiDAR sensor can detect returns.
• Resolution: The density of points in the point cloud, typically measured in points per square meter.
• Return: The reflection of the laser pulse back to the LiDAR sensor.
• Scan pattern: The pattern in which the LiDAR sensor emits laser pulses and receives returns.
• Simultaneous Localization and Mapping (SLAM) use case: LiDAR sensor used in robots and drones to create a map of their environment and locate themselves within it.
• Single photon LiDAR: LiDAR systems that use a single photon detector to detect returns from very long ranges.
• Topographic LiDAR: LiDAR used for creating detailed digital elevation models (DEMs) of the Earth’s surface.