Robot localization aided by quantum algorithms

Localization is a vital aspect of mobile robotics, enabling robots to navigate their environment efficiently and avoid obstacles. Without localization, mobile robots would be unable to determine their position and orientation, making it challenging to plan a path or make informed decisions about their movement (Olson [2000]). Localization allows mobile robots to create an internal map of their environment, which is essential for tasks such as surveying, manipulation, inspection, and delivery (Huang and Lin [2023]). In fact, localization is what enables mobile robots to perform tasks autonomously, making informed decisions about their actions and movements without human intervention. The quality of localization is heavily dependent on the generation of accurate maps, which is a computationally intensive task. Probabilistic localization methods, such as the Adaptive-Monte Carlo localization (AMCL) algorithm, have been widely used in mobile robotics due to their accuracy and robustness (Kristensen and Jensfelt [2003]). However, these methods can be computationally demanding, especially when dealing with large maps or high-resolution sensor data. AMCL, in particular, uses a combination of sensor data and prior map knowledge to determine the probable location of a robot on a given map, but its computation complexity is proportional to the area of the grid of the map (Alshikh Khalil and Hatem [2022]). Recently, the integration of light detection and ranging (LiDAR) sensors has improved the accuracy of localization methods, but the computational requirements remain a challenge (Huang and Lin [2023]).