Improved Robot Localization and Mapping Using Adaptive Tuna Schooling Optimization With Sensor Fusion Techniques

Localization in mobile robotics is essential for achieving autonomy. Effective localization systems integrate data from multiple sensors to enhance state estimation and achieve accurate positioning. Accurate real-time localization is crucial for robot control and trajectory following. Key challenges include initializing the inertial measurement unit (IMU) biases and the direction of gravity, as well as determining the metric scale with a monocular camera. Traditional visual–inertial (VI) initialization techniques rely on precise vision-only motion assessments to address these issues. Multi-sensor fusion faces challenges, such as precise calibration, initialization of sensor groups, and handling measurement errors with varying rates and delays. This paper introduces an Adaptive Tuna Schooling Optimization (ATSO) method to adjust localization strategies based on environmental conditions dynamically. The environmental factors affecting the localization process are considered in the optimization algorithm, and the position is optimally selected accordingly. Using Q-learning with the Q-DNN performs the decision-making process based on past experiences. The dynamic adaptation of the weight parameter allows the algorithm to converge toward optimal solutions, reducing computational complexity. Experimental results demonstrate that the proposed approach improves localization performance, even in challenging conditions.