LiDAR, or Light Detection and Ranging, is a technology that uses laser pulses to measure distances and create 3D maps of the environment. It is widely used in robotics for obstacle avoidance, navigation, mapping, and localization. If you want to learn how to use LiDAR for robotics applications, here are some of the best resources that can help you get started.
Before diving into the details of LiDAR for robotics, you need to understand the basics of how LiDAR works, what are its advantages and limitations, and what are the main types and components of LiDAR systems. A good introduction to LiDAR is this video by The Engineering Mindset, which explains the principles and applications of LiDAR in a clear and concise way. Another useful resource is this article by ROS Industrial, which covers the basics of LiDAR, its integration with ROS (Robot Operating System), and some examples of industrial robots using LiDAR.
Learning about LiDAR (Light Detection and Ranging) for obstacle avoidance in robotics can be a rewarding journey. Here are some excellent resources to get you started: Books: "Introduction to Autonomous Robots: Mechanisms, Sensors, Actuators, and Algorithms" by Nikolaus Correll, Bradley Hayes, et al. "Probabilistic Robotics" by Sebastian Thrun, Wolfram Burgard, and Dieter Fox. Online Courses: Coursera: Robotics Specialization by University of Pennsylvania: This specialization covers various aspects of robotics, including perception and sensing, which is relevant to obstacle avoidance.
1. there are decent learning courses on coursera, udemy, ... 2. online learning stuff on youtube are also helpful 3. if you are working with a specific LiDAR device, its datasheet is also a very good resourse to learn how to use it.
LIDAR - Light Detection and Ranging. Let me tell you where I learn any topic in Robotics: 1. Youtube. My school away from school. 2. Robotics Communities/Forums. Don't under estimate the power of a community. 3. Online platforms especially Coursera. Coursera have a lot of amazing courses for Robotics and its technologies. 4. Ask a friend with the skill. I hope these points help.
LiDAR ( Light Detection and Ranging) is a type of sensor that uses certain wave lengths of light at certain pulses to send and receive a light. So normally a LiDAR is in a light band that is not perceivable by human vision. The sensor sends out a pulse that bounces off a object then returns to the sensor to a detector. The detector is normally tuned to the wavelength of the pulse, so there is less noise from other light sources. The sensor then counts how long it took or if it even received a bounce back. This can allow you to very accurately measure distance. Scanning lidars take this up a notch and have a mirror or other system that allows the sensor to scan on a 2D plane. This can in turn be used to created a map with a computer/program.
Learning about LiDAR for robotics, especially for obstacle avoidance, begins with understanding its fundamentals. Start by exploring how LiDAR functions, its benefits and limitations, and the various types and components of LiDAR systems. A recommended starting point is The Engineering Mindset's video, which provides a clear and concise explanation of LiDAR's principles and applications. Additionally, delve into an article by ROS Industrial, which covers essential LiDAR topics, including its integration with the Robot Operating System (ROS) and showcases examples of industrial robots utilizing LiDAR. These resources will establish a solid foundation in LiDAR technology, aiding in its effective application in robotics for obstacle avoidance.
Once you have a grasp of the fundamentals of LiDAR, you need to learn how to process the raw data that LiDAR sensors produce. LiDAR data is typically represented as point clouds, which are collections of points in 3D space that describe the shape and location of objects. To process point clouds, you need to use specialized software tools and algorithms that can filter, segment, cluster, classify, and register the points. Some of the most popular tools for LiDAR data processing are PCL (Point Cloud Library), PDAL (Point Data Abstraction Library), and CloudCompare. You can find tutorials and documentation for these tools on their respective websites.
LiDAR data is collected through laser pulses that measure the time it takes for the signal to return, providing accurate distance measurements. Common LiDAR data formats include LAS (LiDAR Data Exchange) and LAZ (LAS Zip), which store point cloud information. Each point contains information such as XYZ coordinates, intensity values, and sometimes additional attributes. Raw data may require preprocessing to remove noise, correct for sensor inaccuracies, and filter out unwanted points. This step ensures that the data is accurate and suitable for further analysis. Points in a point cloud can be classified based on their characteristics, such as ground points, vegetation, buildings, and other features.
The instantaneous three-dimensional data acquired by the sensor is referred to as a point cloud and is what is meant to be represented by the term "Frame" in the area of 3D LiDAR. This is analogous to a picture taken by a camera. A typical LiDAR will provide data at some 20 Frames per second. With the appropriate software, you can combine many frames (or point-clouds) to create a 3D Map, that many people call a “Point-cloud 3D Map” LiDAR can be used to measure the distance to almost anything, including terrain, vegetation, buildings, and other moving objects like vehicles and people.
Understanding LiDAR data processing is crucial for robotics. Look for resources that delve into point cloud data, filtering algorithms, and how to interpret LiDAR data to make it usable for robotic applications
One of the main applications of LiDAR for robotics is obstacle detection and avoidance, which is the ability of a robot to sense and avoid collisions with objects in its surroundings. To achieve this, you need to use techniques such as segmentation, clustering, feature extraction, and machine learning to identify and classify obstacles from LiDAR data, and then use planning and control algorithms to generate safe and efficient trajectories for the robot. Some of the best resources for learning obstacle detection and avoidance are this course by Udemy, which teaches you how to use PCL and ROS to implement obstacle detection and avoidance for a mobile robot, and this article by Towards Data Science, which shows you how to use deep learning to detect and avoid dynamic obstacles from LiDAR data.
Once obstacles are identified, the next steps involve planning and control: Planning Algorithms generate optimal paths for the robot, considering the spatial distribution of obstacles. Control Algorithms implement control strategies to ensure the robot follows the planned trajectory safely and efficiently.
Obstacle avoidance task can be different based on the specific scenario you want to implement it in. It has certain algorithms and implementations in each of these circumstances: A wheeled mobile robot, An autonomous car, A humanoid robot, A 4-legged robot, A manipulator robot. After all, the main similarity among all of them is that you need to know the position of other objects during time, as well as the occupying area by the ego robot/vehicle in time. Then you can check their collision.
Robots operate on a variety of modalities, LiDAR being one of them. Crucial challenges come into picture when applying sensor fusion over these modalities. Over-reliance on a certain set of input nodes can send the system on a toss. Autoencoders offer a promising solution to this problem by defining latent space mergeing these modalities. While apparent understanding of latent space is a bit tricky, through multiple iterations, it would be possible to effectively and efficiently utilize multi-modal sensor data.
Mastering obstacle detection and avoidance with LiDAR involves applying techniques like segmentation, clustering, feature extraction, and machine learning to identify and classify obstacles from LiDAR data. Planning and control algorithms are crucial for generating safe and efficient trajectories for the robot. To enhance your skills, consider enrolling in the Udemy course, which focuses on implementing obstacle detection and avoidance for a mobile robot using PCL and ROS. For a deep dive into using deep learning for dynamic obstacle detection and avoidance, explore the article on Towards Data Science. These resources will equip you with the knowledge and practical skills necessary for effective obstacle detection and avoidance in robotics.
The vector histogram algorithm(VHA) measures the distance data between the unknown obstacles and the robot via the lidar sensor, updates the data in real time, and generates a collision free local path for the robot. The method is mainly completed in two stages. In the first stage, the obstacle data collected by the lidar sensor is simplified into a one-dimensional polar coordinate histogram built around the instantaneous position of the robot. In the second stage, the algorithm selects the sector with low obstacle density from the polar histogram, and then aligns the robot's direction of travel to the direction of this safe sector, that is, to determine the direction of motion.
Another important application of LiDAR for robotics is mapping and localization, which is the ability of a robot to create and update a map of its environment, and to estimate its position and orientation within the map. To achieve this, you need to use techniques such as scan matching, loop closure, graph optimization, and SLAM (Simultaneous Localization and Mapping) to fuse LiDAR data with other sensors and odometry information. Some of the best resources for learning mapping and localization are this course by Coursera, which teaches you the fundamentals and applications of SLAM for mobile robots, and this article by Medium, which shows you how to use Google Cartographer, a state-of-the-art SLAM system, with ROS and LiDAR.
Lidar SLAM, or Light Detection and Ranging Simultaneous Localization and Mapping, is a process that enables robots or autonomous systems to build a map of an unknown environment while simultaneously determining their position within that map. SLAM algorithms?typically consist of two main components: a prediction step and a correction step. The prediction step, also known as motion or?odometry?update, estimates the robot's new position based on its previous position and movement information provided by the sensors. The correction step, also known as observation or sensor update, refines the predicted position using measurements from the environment, such as the distances to landmarks detected by the?Lidar sensor
Mapping and localization are advanced LiDAR applications. The best resources explain how robots use LiDAR data to create detailed maps of their surroundings and determine their position within that map
If you want to go beyond the basics and explore some of the advanced topics and projects related to LiDAR for robotics, you can check out some of these resources. This book by Springer, which provides a comprehensive overview of the theory and practice of LiDAR for robotics, covering topics such as calibration, registration, segmentation, classification, object detection, tracking, and scene understanding. This website by Velodyne, which showcases some of the innovative and cutting-edge projects that use LiDAR for robotics, such as autonomous vehicles, drones, delivery robots, and social robots. This blog by Clearpath Robotics, which features some of the latest news and trends in LiDAR for robotics, such as new sensors, applications, and challenges.
LiDAR is a powerful and versatile technology that can enable many robotics applications. By learning how to use LiDAR for robotics, you can enhance your skills and knowledge, and create amazing robots that can interact with the world. We hope that this article has given you some useful and inspiring resources to start your LiDAR learning journey.
Lidar is a very useful sensor for getting information from environment. This data use in multiple reserch and development. Lidar data supports activities such as inundation and storm surge modeling, hydrodynamic modeling, shoreline mapping, emergency response, hydrographic surveying, and coastal vulnerability analysis. These all are advance topic of using lidar. Lidar use in self driving car which gives it better accuracy and perception for motion planning and path planning.
LiDAR technology has many advantages, you can collect data quickly and accurately, its non-invasive nature and its ability to collect data in difficult-to-access areas. In the forestry sector, LiDAR is used to measure tree heights, diameters, and volumes, and to create detailed maps of forest canopies. In the agriculture sector, LiDAR is used to monitor crop health, detect pests and diseases, and optimize irrigation and fertilization. However, the accuracy of LiDAR data can vary depending on factors such as the type of laser used, the scan rate, and the environmental conditions.
When evaluating LiDAR learning also consider the integration of LiDAR data with other sensor information, software compatibility, and the latest advancements in sensor technology to ensure a comprehensive learning experience.