## 2 Analogues Overview
### Existing Calibration Methods  
The paper [3] presents an approach to the camera calibration using MEMS-sensors. The idea is to capture data both from the camera and the gyroscope independently. The authors suggest to calculate the pattern and speed of robot motion using an uncalibrated camera and gyro. Then, they use the grid search method to calculate the correlation between the motion captured using these sensors. Using the least squares method, they estimate the camera focal length and the offset between the camera and the gyro. The disadvantage of such a method is that it works in a one- or two-dimensional space according to the nature of the grid search method. The findings show that the focal length can be calculated accurately with the described method in 2D if the SIFT or ORB feature detector is applied for camera frames. The camera self-calibration for structure from motion was discovered in [4]. In general, this approach also requires feature detection on a camera frame. Due to nonlinearities of calibration parameter estimations, the Sum of Gaussian filter is used to divide the whole nonlinear range of variations into small almost-linear pieces. Further, the authors describe the approach called the Sum of Gaussians. This approach uses several linear filters to cover all almost-linear hypotheses. After that, the authors prune these Gaussians to reduce filters to simple EKF in several steps so the complexity is reduced. Experimental results show that after 150-200 s of work the camera parameters are estimated close to offline calibration parameters. The deviation is not greater than 4%. The disadvantage of such an approach is the algorithmic complexity, since it contains several Kalman filters, and computational time depends on the number of features detectedin a frame. In addition, the described method contains a loop closing component, which also requires computational resources. In [5], the authors track the wheeled mobile robot using an uncalibrated camera. They claim that even manual calibration might be not perfect, and therefore they describe an approach to track the robot's movement using a camera with unknown calibration parameters. The dynamic and kinematic control models were formulated for this robot. The results show that the developed \"global asymptotic position/orientation tracking controller for a wheeled mobile robot\" eliminates the need for integrating the nonlinear kinematic model to achieve its cartesian position. The authors of [6] suggest an approach for automatic extrinsic calibration of multiple cameras without placing any patterns into the environment beforehand. The robot was placed in a natural environment and carried out a set of programmed movements, including a full horizontal rotation, and captured a synchronized image sequence from each camera. The sequences were processed individually with a monocular visual SLAM algorithm. The well known SLAM techniques were applied to build monocular feature maps as the robot made controlled movements. The maps were then matched and aligned in 3D using invariant descriptors and RANSAC to determine the correct correspondences. Then, the final joint bundle adjustment was used to refine estimates and take into account all the features data. The experiments showed that for the three cameras with a 640 \(\times\) 480 resolution, the images with over 80\({}^{\circ}\) field of view achieved 0.1\({}^{\circ}\) of angular error. The disadvantage of this method, as authors claim, is its inability to work with MonoSLAM and a single camera without an intrinsic camera calibration. An interesting approach for calibration of extrinsic and odometric parameters for a differential drive robot can be found in [7]. According to this method, the robots should not move along a specified trajectory and, moreover, it is possible to evaluate calibration parameters of the recorded data. The common idea of this method is based on considering a simultaneous calibration as a maximum-likelihood problem.