Introduction
One of the most important information a robot needs is his position in the environment. In order for a autonomous robot to perform its tasks, his position and orientation must be known. For wheeled mobile robots its position can be classified in two categories: relative positioning and absolute positioning. A relative positioning system is one which uses sensors on the robot's wheels or other navigation systems to calculate the position. An absolute positioning system is one which uses a reference for position determination, such as the Global Positioning Systems (GPS). One of the most fundamental techniques to position determination is the use of encoders on the wheels, which is called Odometry. Although odometry isn't an accurate method to position determination, it is very important to have precise estimates on the wheel's velocity and orientation. Thus, combined with other sensors it makes a reliable way to position calculation. [1]
Odometry
Odometry is used to estimate the change in position over time by the use of data from moving sensors. Odometry is used by robots and autonomous vehicles to estimate their position relative to a starting location. This navigation method is the most used for mobile robot positioning as it provides good short-term accuracy, is inexpensive and allows very high sampling rates. However, this method is sensitive to errors due to integration of velocity measurements over time, which leads inevitably to accumulation of errors. The errors increase proportionality with the distance travelled. [2]
Despite the disadvantages of odometry, this method is very important in navigation, specially associated with other navigation techniques, like visual odometry. Also it provides some important information, like speed, acceleration, distance travelled, and even wheel slippage which are very important information to autonomous tasks, driving assistance, monitoring of risk and manoeuvres aboard a car.
Inertial Navigation
This method measures rate of rotation and acceleration with gyroscopes and/or accelerometers. The measurements must be integrated once or twice to obtain position. This sensors have the advantage that they are self-contained. On the other hand they have the downside of the sensitivity to error because of the need to integrate data to obtain position a small error increases. Inertial navigation are unsuitable for accurate position calculation over a long period of time, also they have a high cost. [3]
Visual Odometry
A vehicle displacement can also be calculated with a camera pointed to the ground using simple mathematics and computer vision algorithms. This method is a good alternative to the traditional encoder on wheels method, with this method we can prevent measurement errors from wheel slippage, changes in tire pressure, tread wear and large tire width. [3]
This method calculates the pixel’s displacement to derive the vehicle motion. Nourani-Vatani et al. [4] used a method based on template matching to calculate pixel’s displacement, their method showed better results than the traditional wheel’s odometry. However their method isn’t good when it encounters sunny or shaded areas, also it has severe limitations as the velocity increases.
Inertial Navigation
This method measures rate of rotation and acceleration with gyroscopes and/or accelerometers. The measurements must be integrated once or twice to obtain position. This sensors have the advantage that they are self-contained. On the other hand they have the downside of the sensitivity to error because of the need to integrate data to obtain position a small error increases. Inertial navigation are unsuitable for accurate position calculation over a long period of time, also they have a high cost. [3]
Visual Odometry
A vehicle displacement can also be calculated with a camera pointed to the ground using simple mathematics and computer vision algorithms. This method is a good alternative to the traditional encoder on wheels method, with this method we can prevent measurement errors from wheel slippage, changes in tire pressure, tread wear and large tire width. [3]
This method calculates the pixel’s displacement to derive the vehicle motion. Nourani-Vatani et al. [4] used a method based on template matching to calculate pixel’s displacement, their method showed better results than the traditional wheel’s odometry. However their method isn’t good when it encounters sunny or shaded areas, also it has severe limitations as the velocity increases.
[2] "Mobile Robot Positioning - Sensors and Techniques" by J. Borenstein
[3] "Where am I? Sensors and Methods for Mobile Robot Positioning" by J. Borenstein, H. R. Everett and L. Feng
[4] "Practical Visual Odometry for Car-like Vehicles" by Navid Nourani-Vatani, Jonathan Roberts and Mandiam V. Srinivasan
[4] "Practical Visual Odometry for Car-like Vehicles" by Navid Nourani-Vatani, Jonathan Roberts and Mandiam V. Srinivasan
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