Relative Altitude Estimation Using Omnidirectional Imaging and Holistic Descriptors - Yerai Berenguer; Luis Payá; David Valiente; Adrián Peidró; Oscar Reinoso (Remote Sensing, 2019)
Currently, many tasks can be carried out using mobile robots. These robots must be able to estimate their position in the environment to plan their actions correctly. Omnidirectional vision sensors constitute a robust choice to solve this problem, since they provide the robot with complete information from the environment where it moves. The use of global appearance or holistic methods along with omnidirectional images constitutes a robust approach to estimate the robot position when its movement is restricted to the ground plane. However, in some applications, the robot changes its altitude with respect to this plane, and this altitude must be estimated. This work focuses on this problem. A method based on the use of holistic descriptors is proposed to estimate the relative altitude of the robot when it moves upwards or downwards. This descriptor is constructed from the Radon transform of omnidirectional images captured by a catadioptric vision system. To estimate the altitude, the descriptor of the image captured from the current position is compared with the descriptor of the reference image, previously built. The framework is based on the use of phase correlation to calculate relative orientation and a method based on the compression-expansion of the columns of the holistic descriptor to estimate relative height. Only an omnidirectional vision sensor and image processing techniques are used to solve these problems. This approach has been tested using different sets of images captured both indoors and outdoors under realistic working conditions. The experimental results prove the validity of the method even in the presence of noise or occlusions.