Robotics: Perception

Go to Course: https://www.coursera.org/learn/robotics-perception

Introduction

# Course Review: Robotics: Perception on Coursera As technology evolves, so do our expectations of what robots can do. The online course **Robotics: Perception**, hosted on Coursera, dives into the fascinating intersection of computer vision and robotics, focusing on how robots perceive the world and navigate through it. This course is an essential stepping stone for anyone interested in robotics, computer vision, or artificial intelligence. ## Course Overview The course begins by addressing a fundamental question: how do robots perceive their surroundings and their own movements to effectively navigate and manipulate objects? Through a structured syllabus, learners explore how cameras mounted on robots transform images into usable data, enabling the extraction of 3D information crucial for navigation and handling tasks. From foundational concepts in image formation to advanced techniques in multi-view geometry, this course covers a spectrum of topics designed to equip students with a comprehensive understanding of robotic perception. ## Syllabus Breakdown 1. **Geometry of Image Formation** The journey begins with a thorough tutorial on camera models. This is crucial for understanding the geometric representation of light and its effect on 3D scenes as perceived by cameras. This module sets the groundwork for students to mathematically grasp how 3D points correlate to 2D images, including the impact of camera movement on image capture. 2. **Projective Transformations** Moving on from basic camera models, students delve deep into the principles of perspective projections. This section illuminates why perception poses challenges, especially in regard to the loss of dimensionality. Learners will explore concepts like vanishing points, which are pivotal for inferring complex information from images. 3. **Pose Estimation** One of the standout components of this course is the focus on feature extraction and pose estimation from image sequences. Students learn to identify prominent features in images and track them across frames, an essential skill in robotics. Practical applications include calculating the camera's position relative to other reference points, utilizing techniques that account for noise and inaccuracies. 4. **Multi-View Geometry** Building on previous modules, this section trains learners in techniques for processing multiple images. By leveraging constraints such as the Epipolar constraint, students will learn to extract relative poses from video data. The concept of Structure from Motion is introduced, allowing participants to compute a camera's trajectory and dynamically adjust their estimates through methods like Bundle Adjustment. ## Recommendations **Who Should Take This Course?** - **Students and Professionals** in robotics, artificial intelligence, or computer vision looking to expand their toolkit. - **Developers** aiming to implement perception capabilities in robotic systems. - **Academics and Researchers** interested in the theoretical aspects of robotic navigation and image processing. **Why Take This Course?** - **Expert Instructors**: The course is taught by experienced professionals in the field, providing insights that are grounded in real-world applications. - **Hands-On Learning**: The practical focus ensures that students engage with the material through exercises and projects, reinforcing the theoretical concepts covered. - **Community & Support**: Being part of a global learning platform means access to forums and discussions that enhance the learning experience. ## Final Thoughts **Robotics: Perception** is an intellectually stimulating course that effectively bridges the gap between theoretical knowledge and practical application in robotics. By the end, learners will have a robust understanding of how robots perceive their environment and will be prepared to tackle challenges in robotic navigation and manipulation. For anyone serious about advancing their knowledge in robotics and computer vision, this course comes highly recommended. Don't miss this opportunity to engage with cutting-edge technology and significantly broaden your skill set!

Syllabus

Geometry of Image Formation

Welcome to Robotics: Perception! We will begin this course with a tutorial on the standard camera models used in computer vision. These models allow us to understand, in a geometric fashion, how light from a scene enters a camera and projects onto a 2D image. By defining these models mathematically, we will be able understand exactly how a point in 3D corresponds to a point in the image and how an image will change as we move a camera in a 3D environment. In the later modules, we will be able to use this information to perform complex perception tasks such as reconstructing 3D scenes from video.

Now that we have a good camera model, we will explore the geometry of perspective projections in depth. We will find that this projection is the cause of the main challenge in perception, as we lose a dimension that we can no longer directly observe. In this module, we will learn about several properties of projective transformations in depth, such as vanishing points, which allow us to infer complex information beyond our basic camera model.

In this module we will be learning about feature extraction and pose estimation from two images. We will learn how to find the most salient parts of an image and track them across multiple frames (i.e. in a video sequence). We will then learn how to use features to find the position of the camera with respect to another reference frame on a plane using Homographies. We will also learn about how to make these techniques more robust, using least squares to hand noisy feature points or RANSAC to remove completely erroneous feature points.

Now we will use what we learned from two view geometry and extend it to sequences of images, such as a video. We will explain the fundamental geometric constraints between point features in images, the Epipolar constraint, and learn how to use it to extract the relative poses between multiple frames. We will finish by combining all this information together for the application of Structure from Motion, where we will compute the trajectory of a camera and a map throughout many frames and refine our estimates using Bundle adjustment.