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David Silver,
Thomas Hossler,
Antje Muntzinger,
Andreas Haja,
Aaron Brown,
Munir Jojo Verge,
and
Mathilde Badoual
In this course, you will learn all about robotic localization, from one-dimensional motion models up to using three-dimensional point cloud maps obtained from lidar sensors. You’ll begin by learning about the bicycle motion model, an approach to use simple motion to estimate location at the next time step, before gathering sensor data. Then, you’ll move onto using Markov localization in order to do 1D object tracking. From there, you will learn how to implement two scan matching algorithms, Iterative Closest Point (ICP) and Normal Distributions Transform (NDP), which work with 2D and 3D data. Finally, you will utilize these scan...
Read more
In this course, you will learn all about robotic localization, from one-dimensional motion models up to using three-dimensional point cloud maps obtained from lidar sensors. You’ll begin by learning about the bicycle motion model, an approach to use simple motion to estimate location at the next time step, before gathering sensor data. Then, you’ll move onto using Markov localization in order to do 1D object tracking. From there, you will learn how to implement two scan matching algorithms, Iterative Closest Point (ICP) and Normal Distributions Transform (NDP), which work with 2D and 3D data. Finally, you will utilize these scan...
Read more
In this course, you will learn all about robotic localization, from one-dimensional motion models up to using three-dimensional point cloud maps obtained from lidar sensors. You’ll begin by learning about the bicycle motion model, an approach to use simple motion to estimate location at the next time step, before gathering sensor data. Then, you’ll move onto using Markov localization in order to do 1D object tracking. From there, you will learn how to implement two scan matching algorithms, Iterative Closest Point (ICP) and Normal Distributions Transform (NDP), which work with 2D and 3D data. Finally, you will utilize these scan matching algorithms in the Point Cloud Library (PCL) to localize a simulated car with lidar sensing, using a 3D point cloud map obtained from the CARLA simulator.
Register for this course and see more details by visiting:
OpenCourser.com/course/t8okrl/localizatio
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What's inside
Syllabus
Meet the team that will guide you through the localization lessons, and learn the intuition behind robotic localization!
Are you ready to build Kalman Filters with C++? Take these quizzes to find out!
Read more
Syllabus
Meet the team that will guide you through the localization lessons, and learn the intuition behind robotic localization!
Are you ready to build Kalman Filters with C++? Take these quizzes to find out!
Read more
Learn the math behind localization, as well as how to implement Markov localization in C++.
Learn about the Point Cloud Library (PCL). Use a simulation highway environment to explore lidar sensing and generate point clouds.
Learn about and build two scan matching algorithms for localization: Iterative Closest Point (ICP) and Normal Distributions Transforms (NDT).
Learn how to align point clouds with ICP and NDT before leveraging them to localize a self-driving car in a simulated environment!
Localize a self-driving car within a point cloud from the CARLA simulator with the localization algorithms you learned in previous lessons - how accurate is your algorithm?
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores robotic localization, which is highly sought-after within the autonomous vehicle industry
Develops core robotics skills using C++, which is a popular language in the field
Emphasizes practical application through simulation environments, enhancing relevance to real-world scenarios
Covers Iterative Closest Point (ICP) and Normal Distributions Transform (NDT), which are commonly used scan matching algorithms
Save this course
Save Localization to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Localization with these
activities:
Review classical mechanics
Show steps
Sharpen your knowledge of classical mechanics to develop a solid foundation for robotics localization concepts.
Browse courses on
Mechanics
Show steps
-
Revisit Newton's laws of motion, conservation of energy, and momentum principles.
-
Practice solving problems involving force, displacement, and energy transformations.
Learn ROS (Robot Operating System)
Show steps
Become familiar with ROS, an essential tool for developing and deploying robotic localization systems in real-world applications.
Browse courses on
ROS
Show steps
-
Follow tutorials to install and configure ROS on your system.
-
Explore ROS packages and nodes for localization, such as the Navigation Stack.
Explore the Point Cloud Library (PCL)
Show steps
Gain familiarity with PCL, a powerful library for working with point cloud data, which is crucial for robotics localization.
Show steps
-
Install and configure PCL on your system.
-
Follow tutorials to explore PCL's capabilities for point cloud processing and visualization.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Implement the bicycle motion model in C++
Show steps
Gain hands-on experience implementing one of the fundamental motion models used in robotics localization.
Show steps
-
Understand the mathematical equations behind the bicycle motion model.
-
Translate the equations into C++ code and test your implementation.
Create a visual representation of a scan matching algorithm
Show steps
Foster a deeper understanding of scan matching algorithms by visualizing their operation in a simulated environment.
Show steps
-
Choose a scan matching algorithm, such as ICP or NDT.
-
Create a simulation environment to generate point cloud data.
-
Implement the scan matching algorithm and visualize the results.
Discuss localization challenges in self-driving cars
Show steps
Engage with peers to explore the complexities and advancements in localization for autonomous vehicles.
Browse courses on
Self-Driving Cars
Show steps
-
Research and gather information about localization challenges in self-driving cars.
-
Participate in discussion forums or organize a study group to share insights and collaborate with peers.
Contribute to an open-source robotics project
Show steps
Gain practical experience and make meaningful contributions to the robotics community by participating in open-source projects.
Browse courses on
Community Involvement
Show steps
-
Identify an open-source robotics project that aligns with your interests.
-
Start contributing to the project by reporting bugs, suggesting improvements, or writing code.
Review classical mechanics
Show steps
Sharpen your knowledge of classical mechanics to develop a solid foundation for robotics localization concepts.
Browse courses on
Mechanics
Show steps
Show steps
Show steps
- Revisit Newton's laws of motion, conservation of energy, and momentum principles.
- Practice solving problems involving force, displacement, and energy transformations.
Learn ROS (Robot Operating System)
Show steps
Become familiar with ROS, an essential tool for developing and deploying robotic localization systems in real-world applications.
Browse courses on
ROS
Show steps
Show steps
Show steps
- Follow tutorials to install and configure ROS on your system.
- Explore ROS packages and nodes for localization, such as the Navigation Stack.
Explore the Point Cloud Library (PCL)
Show steps
Gain familiarity with PCL, a powerful library for working with point cloud data, which is crucial for robotics localization.
Show steps
Show steps
Show steps
- Install and configure PCL on your system.
- Follow tutorials to explore PCL's capabilities for point cloud processing and visualization.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Implement the bicycle motion model in C++
Show steps
Gain hands-on experience implementing one of the fundamental motion models used in robotics localization.
Show steps
Show steps
Show steps
- Understand the mathematical equations behind the bicycle motion model.
- Translate the equations into C++ code and test your implementation.
Create a visual representation of a scan matching algorithm
Show steps
Foster a deeper understanding of scan matching algorithms by visualizing their operation in a simulated environment.
Show steps
Show steps
Show steps
- Choose a scan matching algorithm, such as ICP or NDT.
- Create a simulation environment to generate point cloud data.
- Implement the scan matching algorithm and visualize the results.
Discuss localization challenges in self-driving cars
Show steps
Engage with peers to explore the complexities and advancements in localization for autonomous vehicles.
Browse courses on
Self-Driving Cars
Show steps
Show steps
Show steps
- Research and gather information about localization challenges in self-driving cars.
- Participate in discussion forums or organize a study group to share insights and collaborate with peers.
Contribute to an open-source robotics project
Show steps
Gain practical experience and make meaningful contributions to the robotics community by participating in open-source projects.
Browse courses on
Community Involvement
Show steps
Show steps
Show steps
- Identify an open-source robotics project that aligns with your interests.
- Start contributing to the project by reporting bugs, suggesting improvements, or writing code.
Career center
Learners who complete Localization will develop knowledge and skills
that may be useful to these careers:
Vehicle Localization Engineer
Vehicle Localization Engineer
Vehicle Localization Engineers develop and integrate systems and algorithms that determine the position and orientation of a vehicle. They may work with sensors such as lidar and camera to build a real-time map of the vehicle's surroundings. The Localization course provides a strong foundation in vehicle localization techniques and algorithms, which are essential to the development of autonomous vehicles and other vehicle localization systems.
Mobile Robotics Researcher
Mobile Robotics Researcher
Mobile Robotics Researchers develop new algorithms and technologies for mobile robots. They work on areas such as navigation, mapping, and perception. The Localization course provides a solid foundation in robotic localization algorithms and techniques, which will be essential for you in your research. The course will give you hands-on experience in implementing and evaluating localization algorithms, which will be valuable for your research.
Autonomous Vehicle Engineer
Autonomous Vehicle Engineer
Autonomous Vehicle Engineers design, develop, and test self-driving cars. They develop software and hardware systems that enable cars to navigate the roads safely and efficiently. The Localization course plays a crucial role in self-driving cars. Autonomous Vehicle Engineers must ensure that self-driving cars can accurately determine their position and orientation, which is essential for safe navigation.
Robotic Cartographer
Robotic Cartographer
Robotic Cartographers work in various areas, including mobile Robotics, Unmanned Aerial Vehicle (UAV) systems, and autonomous cars. These professionals develop and deploy high-definition maps for robots and autonomous vehicles. They use multiple sensors such as lidar, radar, camera, and GPS to map environments. The Localization course covers the basics of robotic mapping and localization, including motion models, sensor data, and point cloud mapping. Taking this course will help you develop a foundational understanding of the fundamentals of robotic mapping.
Navigation System Designer
Navigation System Designer
Navigation System Designers develop and implement systems for determining the position, orientation, and direction of movement of objects or people. They work on projects such as GPS navigation systems for cars and smartphones and navigation systems for drones and robots. The Localization course will provide you with the fundamental knowledge of localization and navigation algorithms. This knowledge will be helpful to you in designing and developing navigation systems.
Algorithm Engineer
Algorithm Engineer
Algorithm Engineers design, develop, and implement algorithms to solve specific problems. They work in various areas, including machine learning, computer vision, and robotics. Understanding localization algorithms is crucial for designing algorithms that enable robots and autonomous vehicles to navigate effectively. The Localization course will provide you with a strong foundation in localization algorithms and techniques, which will be fundamental to your work as an Algorithm Engineer.
Localization System Engineer
Localization System Engineer
Localization System Engineers manage and optimize the systems used to determine the location of objects or people. They may work on projects such as indoor navigation systems for shopping malls or hospitals, or tracking systems for wildlife. The Localization course will provide you with fundamental knowledge in localization techniques and algorithms. The course will be useful to you in building and optimizing localization systems.
Perception Engineer
Perception Engineer
Perception Engineers design and develop systems that enable machines to perceive their surroundings. They work in various areas, including autonomous vehicles, robotics, and virtual reality. The Localization course will provide you with a foundational understanding of localization algorithms and techniques. This knowledge will be helpful to you in developing perception systems that can accurately determine the location and orientation of objects and people.
Remote Sensing Scientist
Remote Sensing Scientist
Remote Sensing Scientists use satellite and airborne sensors to collect and analyze data about the Earth's surface. They work in various areas, including land use planning, environmental monitoring, and disaster response. Understanding localization and navigation techniques is essential for Remote Sensing Scientists to accurately geolocate and interpret data collected from satellites and airborne sensors. The Localization course will provide you with a fundamental understanding of localization and navigation algorithms, which will be valuable to you in your work as a Remote Sensing Scientist.
Sensor Fusion Engineer
Sensor Fusion Engineer
Sensor Fusion Engineers design and develop systems that combine data from multiple sensors to create a more accurate and comprehensive understanding of the environment. They work in various areas, including autonomous vehicles, robotics, and wearable devices. The Localization course will provide you with the fundamentals of sensor fusion and localization algorithms, which will be valuable to you in developing and optimizing sensor fusion systems.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and maintain robots. They work in various areas, including industrial automation, healthcare, and space exploration. Understanding robot localization and navigation is critical for Robotics Engineers to enable robots to operate autonomously. The Localization course provides a strong foundation in robotic localization algorithms and techniques, giving Robotics Engineers a basis to develop advanced localization systems.
Cartographer
Cartographer
Cartographers create and maintain maps. They use various tools and techniques to represent geographic information accurately. Knowledge of localization and navigation techniques is essential for Cartographers to create accurate maps of the Earth's surface. The Localization course will provide you with the fundamental understanding of localization and navigation algorithms, which will be a valuable asset in your work as a Cartographer.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and implement algorithms that enable computers to see and understand images. They work in various areas, including image recognition, object detection, and medical imaging. Understanding localization is crucial for Computer Vision Engineers to develop systems that can accurately interpret and navigate their surroundings. The Localization course provides a foundation in robotic localization techniques and algorithms, valuable to Computer Vision Engineers who want to develop vision systems that can accurately determine their location and orientation.
Geodetic Engineer
Geodetic Engineer
Geodetic Engineers use mathematics and technology to determine the size and shape of the Earth and measure the changes that occur on its surface. They work in various areas, including surveying, mapping, and navigation. The Localization course will provide you with the fundamental understanding of localization and navigation algorithms, which will be helpful to you in developing and implementing systems for measuring and tracking the Earth's surface.
Geospatial Analyst
Geospatial Analyst
Geospatial Analysts collect, analyze, and interpret geographic data. They work in various areas, including urban planning, environmental management, and disaster response. Knowledge of localization and navigation techniques is important for Geospatial Analysts to accurately geolocate and map data. The Localization course will provide you with the fundamentals of localization and navigation algorithms, which will be useful to you in your work as a Geospatial Analyst.
For more career information including salaries, visit:
OpenCourser.com/course/t8okrl/localizatio
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Localization.
Comprehensive reference on probabilistic robotics, covering topics such as motion models, sensor models, and localization algorithms. It is suitable for both beginners and advanced students, and it is often used as a textbook in university courses on robotics.
Provides a detailed overview of SLAM, with a focus on algorithms and applications. It is suitable for advanced students and researchers, and it is often used as a textbook in university courses on SLAM.
Provides a comprehensive overview of robot modeling and control, with a focus on kinematics, dynamics, and control algorithms.
Provides a comprehensive overview of Kalman filtering, with a focus on applications in robotics.
Provides a comprehensive overview of robot dynamics and control, with a focus on nonlinear systems.
Save
Provides a comprehensive overview of planning algorithms, with a focus on motion planning and robot navigation.
Provides a comprehensive overview of robot manipulators, with a focus on modeling, control, and applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/t8okrl/localizatio
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Time
40320 hours
Level
Advanced
Via
Udacity
Instructors
David Silver
Thomas Hossler
Antje Muntzinger
Andreas Haja
Aaron Brown
Munir Jojo Verge
Mathilde Badoual
Language
English
Good to know
Explores robotic localization, which is highly sought-after within the autonomous vehicle industry
Develops core robotics skills using C++, which is a popular language in the field
Emphasizes practical application through simulation environments, enhancing relevance to real-world scenarios
Covers Iterative Closest Point (ICP) and Normal Distributions Transform (NDT), which are commonly used scan matching algorithms
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