Update ch1.md

ch1.md

@@ -8,11 +8,11 @@ So, what is SLAM?
 
 SLAM stands for **S**imultaneous **L**ocalization **a**nd **M**apping. It usually refers to a robot or a moving rigid body, equipped with a specific **sensor**, estimates its own **motion** and builds a **model** (certain kinds of description) of the surrounding environment, without a priori information [Davison2007]. If the sensor referred here is mainly a camera, it is called "Visual SLAM".
 
-Visual SLAM is the subject of this book. We deliberately put a long definition into one single sentence, so that the readers can have a clearer concept. First of all, SLAM aims at solving the "positioning" and "map building" issues at the same time. In other words, it is a problem of how to estimate the location of a sensor itself, while estimating the model of the environment. So how to achieve it? This requires a good understanding of sensor information. A sensor can observe the external world in a certain form, but the specific approaches for utilizing such observations are usually different. And, why does this problem worth spending an entire book to discuss? Simply because it is difficult, especially if we want to achieve successful SLAM in **real time** and **without any a priory knowledge**. When we talk about visual SLAM, we need to estimate the trajectory and map based on a set of continuous images (which form a video).
+Visual SLAM is the subject of this book. We deliberately put a long definition into one single sentence, so that the readers can have a clearer concept. First of all, SLAM aims at solving the "positioning" and "map building" issues at the same time. In other words, it is a problem of how to estimate the location of a sensor itself, while estimating the model of the environment. So how to achieve it? This requires a good understanding of sensor information. A sensor can observe the external world in a certain form, but the specific approaches for utilizing such observations are usually different. And, why does this problem worth spending an entire book to discuss? Simply because it is difficult, especially if we want to achieve successful SLAM in **real time** and **without any prior knowledge**. When we talk about visual SLAM, we need to estimate the trajectory and map based on a set of continuous images (obtained form a video).
 
 This seems to be quite intuitive. When we human beings enter an unfamiliar environment, aren't we doing exactly the same thing? So, the question is whether we can write programs and make computers do so. 
 
-At the birth of computer vision, people imagined that one day computers could act like human, watching and observing the world, and understanding the surrounding environment. The ability of exploring unknown areas is  a wonderful and romantic dream, attracting numerous researchers striving on this problem day and night [Hartyley2003]. We thought that this would not be that difficult, but the progress turned out to be not as smooth as expected. Flowers, trees, insects, birds and animals, are recorded so differently in  computers: they are  simply matrices consist of numbers. To make computers understand the contents of images, is as difficult as making us human understand those blocks of numbers. We didn't even know how we understand images, nor do we know how to make computers do so. However, after decades of struggling,  we finally started to see signs of success - through Artificial Intelligence (AI) and Machine Learning (ML) technologies, which gradually enable computers to identify objects, faces, voices, texts, although in a way (probabilistic modeling) that is still so different from us. On the other hand, after nearly three decades of development in SLAM, our cameras begin to capture their movements and know their positions, although there is still a huge gap between the capability of computers and human. Researchers have successfully built a variety of real-time SLAM systems. Some of them can efficiently track their own locations, and others can even do three-dimensional reconstruction in real-time.
+At the birth of computer vision, people imagined that one day computers could act like humans, watching and observing the world, and understanding the surrounding environment. The ability of exploring unknown areas is  a wonderful and romantic dream, attracting numerous researchers striving on this problem day and night [Hartyley2003]. We thought that this would not be that difficult, but the progress turned out to be not as smooth as expected. Flowers, trees, insects, birds and animals, are recorded differently in  computers: they are  simply matrices consisting of numbers. To make computers understand the content of images, is as difficult as making us human understand those blocks of numbers. We dont even know how we understand images, nor do we know how to make computers do so. However, after decades of struggling,  we finally started to see signs of success - through Artificial Intelligence (AI) and Machine Learning (ML) technologies, which gradually enable computers to identify objects, faces, voices, texts, although in a way (probabilistic modeling) that is still so different from us. On the other hand, after nearly three decades of development in SLAM, our cameras began to capture their own movements and know their own positions, although there is still a huge gap between the capability of computers and human. Researchers have successfully built a variety of real-time SLAM systems. Some of them can efficiently track their own locations, and others can even do three-dimensional reconstruction in real-time.
 
 This is really difficult, but we have made remarkable progress. What's more exciting is that, in recent years, we have seen emergence of a large number of SLAM-related applications. The sensor location could be very useful in many areas: indoor sweeping machines and mobile robots, automatic driving cars, Unmanned Aerial Vehicles (UAVs) in the air, Virtual Reality (VR) and Augmented Reality (AR). SLAM is so important. Without it, the sweeping machine cannot maneuver in a room autonomously, but wandering blindly instead; domestic robots can not follow instructions to reach a certain room accurately; Virtual Reality will always be limited within a prepared space. If none of these innovations could be seen in real life, what a pity it would be.