SLAM for Dummies.pdfSLAM for Dummies, SLAM入门级教程，全英

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详细说明：SLAM for Dummies, SLAM入门级教程，全英文可复制，共127页13 REFERENCES: ,垂看。。。。。●4。4。。看e。。看·看· 看s。。。。垂。l。垂。业。·。。。。●垂 42 APPENDIX A: COORDINATE CONVERSION……43 15. APPendIX B: SICK LMS 200 INTERFACE CODE 16 APPENDIX C: ER1 INTERFACE CODE 2 17. APPENDIX D: LANDMARK EXTRACTION CODE 82 2 Introduction The goal of this document is to give a tutorial introduction to the field of SLAM (Simultaneous Localization And Mapping) for mobile robots. There are numerous papers on the subject but for someone new in the field it will require many hours of rescarch to understand many of the intricacies involved in implementing SLAM. The hope is thus to present the subject in a clear and concise manner while keeping the prerequisites required to understand the document to a minimum. It should actually be possible to sit down and implement basic SLam after having read this paper SLAM can be implemented in many ways. First of all there is a huge amount of different hardware that can be used. Secondly slam is more like a concept than a single algorithm. There are many steps involved in SLaM and these different steps can be implemented using a number of different algorithms. In most cases we explain a single approach to these different steps but hint at other possible ways to do them for the purpose of further reading The motivation behind writing this paper is primarily to help ourselves understand SLAM better. One will always get a better knowledge of a subject by teaching it Second of all most of the existing SLAM papers are very theoretic and primarily focus on innovations in small areas ofslaM, which of course is their purpose. The purpose of this paper is to be very practical and focus on a simple, basic SLaM algorithm that can be used as a starting point to get to know Slam better. For people with some background knowledge in Slam we here present a complete solution for SLAM using EKF(Extended Kalman Filter). By complete we do not mcan perfcct What we mean is that we cover all the basic steps required to get an implementation up and running. It must also be noted that Slam as such has not been completely solved and there is still considerable research going on in the field To make it easy to get started all code is provided, so it is basically just a matter of downloading it, compiling it, plugging in the hardware(SICK laser scanner, eRI robot) and executing the application; Plug-and-Play. We have used Microsoft visual C# and the code will compile in the. Net Framework v 1. 1. Most of the code is very straightforward and can be read almost as pseudo-code, so porting to other languages or platforms should be easy 3. About sLAM The term SLAM is as stated an acronym for Simultaneous Localization And Mapping. It was originally developed by hugh durrant-Whyte and John J. Leonard [7]based on earlier work by Smith, Self and Cheeseman [6]. Durrant-Whyte and Leonard originally termed it smal but it was later changed to give a better impact SLAM is concerned with the problem of building a map of an unknown environment by a mobile robot while at the same time navigating the environment using the map SLAM consists of multiple parts; Landmark extraction, data association, state estimation, state update and landmark update. There are many ways to solve each of the smaller parts. We will be showing examples for each part. This also means that some of the parts can be replaced by a new way of doing this. As an example we will solve the landmark extraction problem in two different ways and comment on the methods. The idea is that you can use our implementation and extend it by using your own novel approach to these algorithms. We have decided to focus on a mobile robot in an indoor environment. You may choose to change some of these algorithms so that it can be for example used in a different environment SLAM is applicable for both 2D and 3D moLion. We will only be considering 2D motion It is helpful if the reader is already familiar with the general concepts of SlaM on an introductory level, e.g. through a university level course on the subject. There are lots of great introductions to this field of research including [6 [4]. Also it is helpful to know a little about the extended Kalman Filter(EK F); sources of introduction are 33]5]. Background information is always helpful as it will allow you to more easily understand this tutorial but it is not strictly required to comprehend all of it 4. The hardware The hardware of the robot is quite important to do slam there is the need for a mobile robot and a range measurement device. The mobile robots we consider are wheeled indoor robots. This documents focus is mainly on so flare implementation of sLaM and docs not explore robots with complicated motion models(models of how the robot moves) such as humanoid robots, autonomous underwater vehicles autonomous planes, robots with weird wheel configurations etc We here present some basic measurement devices commonly used for SLaM on mobile robots The robot Important parameters to consider are ease of use, odometry performance and price The odometry performance mcasures how well the robot can estimate its own position, just from the rotation of the wheels. The robot should not have an error of more than 2 cm per meter moved and 2 per 45 degrees turned. Typical robot drivers allow the robot to report its(x, y) position in some Cartesian coordinate system and also to report the robots current bearing/heading There is the choice to build the robot from scratch. This can be very time consuming but also a learning experience. It is also possible to buy robots ready to use, like real World Interface or the Evolution Robotics ERl robot [10]. The rwI is not sold anymore, though, but it is usually available in many computer science labs around the world. The RWI robot has notoriously bad odometry, though. This adds to the problem of estimating the current position and makes SLaM considerably harder ERI is the one we are using. It is small and very cheap It can be bought for onl 200USD for academic use and 300USd for private use. It comes with a camera and a robot control system. We have provided very basic drivers in the appendix and on the website The range measurement device The range measurement device used is usually a laser scanner nowadays they are very precise, efficient and th he output does not require much computation to process On the downside they are also very expensive. A SICK scanner costs about 5000USD. Problems with laser scanners are looking at certain surfaces including glass, where they can give very bad readings(data output). Also lascr scanners cannot be used underwater since the water disrupts the light and the range is drastically reduced Second there is the option of sonar. Sonar was used intensively some years ago. The are very cheap compared to laser scanners. Their measurements are not very good compared to laser scanners and they often give bad readings. Where laser scanners have a single straight line of measurement emitted from the scanner with a width of as little as 0.25 degrees a sonar can easily have beams up to 30 degrees in width Underwater, though, they are the best choice and resemble the way dolphins navigate The type used is often a Polaroid sonar. It was originally developed to measure the distance when taking pictures in Polaroid cameras. Sonar has been successfully used in[7] The third option is to use vision. Traditionally it has been very computationally intensive to use vision and also error prone due to changes in light. Given a room without light a vision system will most certainly not work. In the recent years, though, there have been some interesting advances within this field Often the systems use a stereo or triclops system to measure the distance Using vision resembles the way humans look at the world and thus may be more intuitively appealing than laser or sonar. Also there is a lot more information in a picture compared to laser and sonar scans. This used to be the bottleneck, since all this data needed to be processed, but with advances in algorithms and computation power this is becoming less of a problem. Vision based range measurement has been successfully used in [8] We have chosen to use a laser range finder from SICK [9]. It is very widely used, it is not dangerous to the eye and has nice properties for use in SLaM. The measurement error is +-50mm, which seems like very much, but in practice the error was much smaller the newest laser scanners from sick have measurement errors down to t-5 II 5. The slaM Process The SLaM process consists of a number of steps. The goal of the process is to use the environment to update the position of the robot. Since the odometry of the robot (which gives the robots position) is often erroneous we cannot rely directly on the odometry. We can use laser scans of the environment to correct the position of the robot. This is accomplished by extracting features from the environment and re observing when the robot moves around. An eKF(Extended Kalman Filter) is the heart of the SLAM process. It is responsible for updating where the robot thinks it is based on these features. These features are commonly called landmarks and will be explained along with the eKF in the next couple of chapters. The EKF keeps track of an estimate of the uncertainty in the robots position and also the uncertainty in these landmarks it has seen in the environment An outline of the SLAM process is given below Laser scan Landmark Odometry change Extraction EKF Dala Odometry update association EKF Re-observation EKF New observations Figure 1 Overview of the SLAM process

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