This week we will learn about self-localization techniques including the particle filter. In this first lecture, we will consider models for odometry as a first order approximation to the robot's location. As in your car, where the odometer records how many miles you have traveled, odometry provides a measurement of how far the robot has moved. Odometry is just one method of finding the robot's location in the world. If we look at a typical application of localization, car navigation, we see several ways to find location. Information sources include GPS, global positioning system, cellular networks, and Wi-Fi access points. Each of these sources, however, have certain levels of noise that lead to various levels of accuracy. Driverless cars, for instance, will need better than 3.5 meters of accuracy that the GPS provides. That error is the difference between occupying the sidewalk and the road. The previous sources represent global knowledge of position, exact coordinates. Odometry and other sources of information can augment the global localization sources with local knowledge. How have they changed coordinates? These sources of information are more precise, giving centimeter accuracy. However, integrating sources, like encoders and gyroscopes, over time can lead to drift. This is due to the accumulation of errors in time. Errors from slippage of the wheels deceive the encoder for instance. Other local sensors like laser scanners and color and depth cameras can help to correct these errors. We will see how this incorporation happens later in the week. Odometry updates start with modeling the robot. Different robots, such as humanoids or aerial vehicles, will require different models. In our case, we will model a skid steer four-wheeled robot. The odometry measurements come from ticks from the encoder that measure how much the wheels have rotated in a given timeframe. These ticks can be mapped into translation and rotation of the body of the robot. First, let's explore the rotation odometry calculation. With a skid steer robot, the left and right sets of wheels are controlled independently. When turning, these two sides form the inner and outer radii of circles that share the same center. Coupled with the knowledge of how wide the robot vehicle is, we can determine a change in angle based on these encoder ticks. First, we want to translate motor ticks into meters traveled by the inner and outer wheels along their respective arcs. This conversion requires knowledge of the wheel sizes. Here, these measurements in meters are denoted eo and ei. The inner and outer arcs are known, but they also share the same angle of rotation. With knowledge of the width of the robot, we can use the difference in arcments to calculate the shared angle data. Next, we will consider the translation of the robot. Conveniently, the translation requires knowledge of the rotation that we have already calculated. In measuring translation, we can form a triangle with the known angle of rotation. We then can average the change in position for both the inner and outer wheel sets to find the change in the x direction. The change in the y direction requires a similar methodology. For small movements, this is a good approximation for the translation. Unfortunately, the encoder measurements can be noisy due to wheel slippage. Angular estimates then propagate errors into the translation estimates. One solution to this problem is to utilize the gyroscope to find a more precise measurement of angular change. For a small number of time steps, the gyroscope can be very accurate. Thus, angular odometry is measured solely by the rate of change observed by the gyro, integrated over time. This measurement aids in translation calculations as well. This simple odometry approach to localization requires a frame of reference for where the robot began its trip. Local measurements from the encoders and gyroscopes still provide noisy estimates. So we want to include more measurements to correct errors. The next sections will discuss using maps to aid in localization correction, as well as ways to probabilistically define our localization state.
