How can LiDAR be used to correct errors in robotic navigation?

One thing that I have found helpful is using the safety zone field sets in order to capture the detection of objects before the robot or the human being has been impacted. This programming of the zones assist in error detection and play a vital role in the overall risk assessment and risk mitigation plan based upon job role. The big question we have to be honest in stating is………Have we completed a risk assessment during the design phase that is then challenged upon system completion ??????? If not …..no need to sweat .. just get started??! Safety must be a continuous effort embraced by all disciplines within a successful organization!

In the domain of robotics, accurate navigation is crucial for enabling robots to execute their tasks effectively. However, navigating complex environments can introduce errors that hinder the robot's performance. LiDAR has emerged as a powerful sensor for correcting these errors and enhancing navigational accuracy. LiDAR, a sensor that emits laser pulses to create high-resolution 3D point clouds, plays a crucial role in error correction for robotic navigation. By filtering out noise and outliers, smoothing jagged edges, registering new scans with existing maps, identifying loop closures, and proactively detecting and correcting errors, LiDAR enables robots to navigate complex environments with greater precision and reliability.

Its interesting, a laser mouse uses imagery and comparison of images vs time so it can establish how it moved. It seems if the drone had good clear imagery of what was under it, the drone could run this laser mouse process backwards. Starting out from its launch point, which is a known geographic location, the drone could fly along looking down and taking enough sequential images to allow the drone to calculate where it is currently located with respect to the launch point. This would allow the drone to navigate itself without any need for GPS and without any requirement that it have stored current imagery of the ground beneath it. The only problem is if the drone could not do accurate enough imaging for some reason for an interval of time

La calibración LiDAR es como afinar un instrumento para obtener la mejor melodía. Ajustar los parámetros del sensor, ya sea recopilando datos en un entorno conocido o mientras el robot se mueve, es clave para mejorar la precisión y consistencia de las mediciones. Es como corregir peque?os errores para que los datos LiDAR sean más confiables, evitando desalineaciones y distorsiones. ?Más precisión, menos problemas!

LiDAR calibration corrects systematic errors and misalignments in LiDAR data for accurate 3D measurements. It's essential for autonomous driving, robotics, and surveying. The main LiDAR calibration methods are intrinsic and extrinsic. Intrinsic calibration addresses LiDAR sensor intrinsic defects like laser beam distortion, detector non-uniformity, and component misalignment. LiDAR vendors supply sensor-specific calibration coefficients for raw data processing. Extrinsic calibration compares the LiDAR sensor's position and orientation to other sensors or reference points. LiDAR calibration improves 3D measurements, object detection, and environment perception and mapping.

LiDAR error models assist quantify data uncertainty by mathematically representing flaws that can affect measurements. Range error models assume random errors, deterministic models forecast and adjust for errors, and hybrid models incorporate stochastic and deterministic aspects. Angle error models include angular bias, intensity bias, timing error, and point cloud distortion. Adding LiDAR error models requires parameter estimation, data rectification, and uncertainty propagation. Specialised methods estimate parameters using calibration data or field measurements. After learning the error model, LiDAR data can be corrected and improved.

Personalmente imagino los modelos de error LiDAR como detectives matemáticos que desentra?an los misterios detrás de los peque?os deslices. Hay dos tipos: los errores sistemáticos, que son como patrones predecibles causados por cosas como temperatura o defectos de hardware, y los errores aleatorios, que son más impredecibles, como ruido o reflejos inesperados. Estos modelos actúan como herramientas inteligentes que no solo cuantifican, sino que también compensan estos errores, mejorando la robustez y confiabilidad del sistema

LiDAR error models quantify inaccuracies in measurements, including range, geometric, angular, and atmospheric errors. Range errors stem from instrumental limits and environmental factors. Geometric errors arise from inaccuracies in sensor positioning. Angular errors are due to misalignment in angle measurements. Atmospheric conditions affect the light pulse, causing atmospheric errors. Sensor noise also contributes to overall inaccuracy. Understanding these errors is crucial for improving LiDAR data accuracy and reliability.

Os modelos de erro LiDAR classificam os desvios em sistemáticos (consistentes e previsíveis, como temperatura) e aleatórios (imprevisíveis, como ruído). Esses modelos quantificam e compensam erros, melhorando a confiabilidade da navega??o LiDAR ao considerar fatores variados, promovendo uma compreens?o abrangente e robusta dos dados coletados.

LiDAR error models help in understanding and quantifying sensor inaccuracies. These models are used to predict and compensate for errors, reducing their impact on navigation decisions. Algorithms use these error models to adjust LiDAR data in real-time, correcting measurements to improve accuracy. This ensures that navigation decisions are based on more reliable data, reducing errors caused by sensor limitations.

LiDAR-based localization uses LiDAR sensors to locate a vehicle or robot in its environment. It uses laser pulses to acquire LiDAR data, extract features, match them to a map, and estimate the vehicle's location and orientation using algorithms like ICP and SLAM. LiDAR is ideal for autonomous cars, robots, surveying, and mapping due to its accuracy, 3D perception, and environmental durability. Self-driving cars, warehouse robots, and mapping for building, infrastructure inspection, and archaeology are applications. Overall, LiDAR-based localization improves several businesses.

At work, I saw LiDAR-based localization significantly improve robotic navigation. By generating precise 3D scans, LiDAR helps robots accurately determine their location, especially in GPS-denied areas like indoors. This method involves comparing real-time LiDAR data with pre-existing maps for precise positioning. It's crucial for correcting navigation drift or inaccuracies, ensuring reliable movement in complex environments. LiDAR-based localization is key for autonomous systems needing exact location awareness, enhancing overall navigational accuracy.

In addition, we need to refer to landmarks to identify the current robot position, but it's really important to note how to avoid registering moving objects as landmarks; LiDARs can't specify which object is moving and what is still

I would like to add that in absolute localization, LiDAR uses special programs to match its current data with stored maps. This is helpful in places where things change often, and the system needs to keep updating what it knows about the area. Also, combining LiDAR data with information from cameras or GPS can make it work better, especially in tricky situations like when things look the same or it's hard to see.

LiDAR-based localization involves using LiDAR sensors to map an environment and determine a device's position within it. It captures precise distance measurements to surrounding objects, creating a 3D point cloud. By comparing this data with a pre-existing map or continuously updating the map, the system identifies its location and orientation, essential for navigation in robotics, autonomous vehicles, and spatial analysis. This method offers high accuracy and detail in diverse conditions, making it a key technology for real-time localization.

LiDAR based mapping is a methodology that will play a role in determining our Factory throughput strategy, typically it is not included within the OEE equation due to the inconsistency,although with so much focus being put on robust algorithms capturing detailed information, I cannot help but get excited over the impact it can have over the next few years of maturity.

LiDAR-based mapping is vital in robotic navigation, offering high-resolution 3D environmental mapping. It enables robots to create detailed and accurate representations of their surroundings, crucial for navigating complex terrains. This technology is especially beneficial in areas lacking clear visual landmarks or GPS signals. By emitting laser pulses and measuring their reflections, LiDAR provides depth information, creating a dynamic map that assists robots in obstacle detection and avoidance. It's also used for building persistent maps in long-term navigation tasks, aiding in both initial exploration and ongoing environment monitoring. This advanced mapping capability is key to enhancing autonomous navigation in diverse settings.

LiDAR-based mapping creates realistic 3D maps of environments using LiDAR sensors. Data gathering, preprocessing, registration, feature extraction, map representation, and refinement occur. Clean and filter the raw LiDAR point cloud to remove noise, outliers, and superfluous points. Registration and alignment ensure map uniformity and accuracy. Map representation and localization require feature extraction. Octomaps, gridmaps, and featuremaps are map representations. Iterative map refinement improves accuracy and completeness. LiDAR mapping has excellent precision, dense point clouds, direct distance measurements, and environmental robustness. Autonomous cars, robotics, mapping, surveying, forest management, and urban planning.

LiDAR-based mapping makes a detalied model of the area using LiDAR data. And here we are having two types: geometric and semantic. Geometric mapping uses shapes like points, lines, and surfaces to create the map. Semantic mapping uses things like objects and labels for mapping. Have a look at the different visualtiations (use google to get a better idea) This method gives a lot of detail about the area but needs corrections for errors like noise or changes in the environment.

La asignación basada en LiDAR es como crear un mapa detallado del mundo real usando la magia de los datos LiDAR. Puedes elegir entre resaltar las formas y líneas del entorno (geométrico) o identificar cosas específicas como árboles o autos (semántico). Es un poco como ser un cartógrafo moderno que captura no solo las calles, sino también el "espíritu" de lo que hay en ellas. Pero, claro, como en toda aventura, debes ser hábil para corregir peque?os errores causados por ruidos o cambios inesperados. Al final, obtienes un mapa mágico que revela tanto la estructura como la esencia de tu entorno.

LiDAR-based path planning helps a robot to find the best route to its goal using LiDAR. It uses global planning, considering the whole map to plot a course from start to end, and local planning, focusing on obstacles that occur during movement, using recent data. This method ensures safe, efficient movement, but it must correct errors to adapt to new situations or prevent crashes.

Robots and autonomous vehicles navigate various situations safely and precisely using LiDAR-based path planning. High-resolution 3D maps and LiDAR obstacle detection help it avoid collisions and navigate complex areas quickly. The procedure includes LiDAR data collection, map construction, path planning algorithms, optimisation, and obstacle avoidance. High precision, 3D environment awareness, dynamic obstacle identification, and versatility are LiDAR-based path planning benefits. Delivery, exploration, service, and autonomous vehicles are applications. LiDAR-based path planning is applied in outdoor landscapes, busy industrial floors, and dim warehouses.

Obstacle avoidance is one of the most important aspects to consider when planning a robot’s path, and LiDAR sensors are very effective in accomplishing this task. To detect obstacles in a robot’s path, LiDAR generates data points called point clouds that can be used to create 3D models of the sensed objects or environments. The information extracted from point clouds can be used by a robot to determine where obstacles are located in its path so that it can avoid them.

Lidar goes hand in hand with the establishment of a map for navigation, by generating the map while determining its location: the process is called SLAM (Simultaneous Localization And Mapping). The principle is simple: the robot is runing along a logic where the path is exploratory. Based on its own localization, the robot is using IMU and odometry as well as a partial map realized simultaneously , to correct both orientation and location. The map gets more and more complex in details along teh robot motion, and the robot uses gates detected in the map, thanks to the Lidar, to pursue completing the map by exploring areas not yet visited. This process can be optimized (in exploration) if the robot is guided manually in the regions to mapped.

LiDAR-based path planning utilizes LiDAR data to navigate and determine the safest, most efficient route for movement, particularly in autonomous vehicles and robotics. It scans the environment, creating a 3D map with real-time obstacle detection. Algorithms process this data to identify viable paths, avoid collisions, and adapt to dynamic changes. This technology is essential in complex environments, ensuring accurate, real-time decision-making for safe navigation, optimizing routes, and enabling machines to understand and interact with their surroundings effectively.

Lidar-based error correction in robot navigation involves refining sensor measurements to enhance accuracy. As a robotics enthusiast, this technological advancement excites me because this correction is crucial for precise mapping and obstacle avoidance. By mitigating inaccuracies in lidar data, the robot can improve its overall navigation performance, ensuring more reliable and safe movement in its environment.

Lidar like every other sensor needs calibration, and is prone to errors: the errors can be produced by moise, light, magnetic reflectance and metallic objects facing the lidar beams. Also, absorbing material and rough/cavitated materials can reduce accuracy by reducing the lidar readings. Kalman filtering can help smoothening the data read and compensate for missing data (prediction)... The superposition of lidar reading by IMU, GPS, UWB and odometry can help correct the way mapping is determined by lidar alone and correct distortion of maps due to lack of pose information and drift of sensing hysteresis (for the ones depending on electromagnetic properties).

Besides the strong presence of LiDAR in the research field of robotics, its application in the real world is scarce. LiDAR is still an expensive and fragile technology. It can't handle the adversities of real outdoor environment, like vibration, dust, snow, mud... The industry is going with cameras and radars to overcome the real world challenges.

Aujourd'hui la technologie LIDAR permet une surveillance de zone sure avec un arrêt immédiat du robot ou cobot , la directive machine doit évolué sur le point de détection réel / au point de detection calculé par formule trop éloigné de la réalité demandant une zone réglé trop importante par rapport à l'espace disponible chez le clients ( zone parfois trop petite car beaucoup trop encombré ) Une vidéo du régla effectué peut être assimilé à une preuve de cohérence que le réglage effectué ne respectant pas les calculs fournit par les fabricant et conforme aux spécificités demandés par la norme

One of the ways LiDAR can be used for robotic navigation, is in the bone surgeries. A preoperative 3D model of a bone; on which a surgeon already planned bone resection planes/paths, implant position etc; can be registered with a instantaneous 3D point cloud of the same bone captured intraoperatively using the LiDAR. This 3D/3D registration can then be used to guide a robot or a surgeon's tool (tracked with optical navigation like NDA) to make accurate bone resection or implant positioning. The LiDAR accuracy is however, still limited to achieve great registration and hence surgical accuracies (maybe within 2 mm). Intel's other stereo cameras might give better accuracy, but LiDAR will have more accurate versions in the future.

LIDAR technology can be used in areas beyond robotics. There are freely available databases of satellite images taken using LIDAR. The heightmap produced through LIDAR can be used, for example, to quickly create maps in game development engines such as Unreal Engine. This way a lot of time is saved. As there are many LIDAR satellite images available for free, usually distributed by government organizations, a lot of money is also saved on projects that need these images.

We also need to consider the limitations within the LiDAR model itself, more expensive models provide more data points and wider pov, so LiDAR selection should be based on budget and project requirement