Inertial measurement unit (IMU) comprising of the accelerometer and gyroscope is prone to various deterministic errors like bias, scale factor, and nonorthogonality, which need to be calibrated carefully. In this paper, a survey has been carried out over different calibration techniques that try to estimate these error parameters. These calibration schemes are discussed under two broad categories, that is, calibration with high-end equipment and without any equipment. Traditional calibration techniques use high-precision equipment to generate references for calibrating inertial sensors and are generally laboratory-based setup. Inertial sensor calibration without the use of any costly equipment is further studied under two subcategories: ones based on multiposition method and others with Kalman filtering framework. Later, a brief review of vision-based inertial sensor calibration schemes is also provided in this work followed by a discussion which indicates different shortcomings and future scopes in the area of inertial sensor calibration.

Histogram equalisation has been a much sought‐after technique for improving the contrast of an image, which however leads to an over enhancement of the image, giving it an unnatural and degraded appearance. In this framework, a generalised contrast enhancement algorithm is proposed which is independent of parameter setting for a given dynamic range of the input image. The algorithm uses the modified histogram for spatial transformation on grey scale to render a better quality image irrespective of the image type. Added to this, two variants of the proposed methodology are presented, one of which preserves the brightness of original image while the other variant increases the image brightness adaptively, giving it a better look. Qualitative and quantitative assessments like degree of entropy un‐preservation, edge‐based contrast measure and structure similarity index measures are then applied to the 500 image data set for comparing the proposed algorithm with several existing state‐of‐the‐art algorithms. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several algorithms.

Attitude estimation is the process of computing the orientation angles of an object with respect to a fixed frame of reference. Gyroscope, accelerometer, and magnetometer are some of the fundamental sensors used in attitude estimation. The orientation angles computed from these sensors are combined using the sensor fusion methodologies to obtain accurate estimates. The complementary filter is one of the widely adopted techniques whose performance is highly dependent on the appropriate selection of its gain parameters. This paper presents a novel cascaded architecture of the complementary filter that employs a nonlinear and linear version of the complementary filter within one framework. The nonlinear version is used to correct the gyroscope bias, while the linear version estimates the attitude angle. The significant advantage of the proposed architecture is its independence of the filter parameters, thereby avoiding tuning the filter’s gain parameters. The proposed architecture does not require any mathematical modeling of the system and is computationally inexpensive. The proposed methodology is applied to the real-world datasets, and the estimation results were found to be promising compared to the other state-of-the-art algorithms.