Data Acquisition analysis in SLAM applications

摘要

The Simultaneous Localization and Mapping (SLAM) problem for mobile robots aims at consistently building a map of an unknown environment while simultaneously determining its position or location within this map. From a control-theory viewpoint, it is somehow analogous to simultaneously estimating the states and output map of the system. In the robotic based engineering applications, SLAM is arguably considered a solved problem on a theoretical and conceptual level, but still it requires considerable maturity on a practical level [16][22]. The state-of-the-art SLAM algorithms require computationally powerful processors, expensive sensors with dense feature extraction and multiple sensors for uncertainty reduction [21]. An approach to the SLAM problem using minimal sensing information is still lacking in both theoretical and practical aspects. This paper highlights the data acquisition & association issues with different sensors like odometric & infra red sensors used in SLAM application due to addition of random Gaussian noise leading to errors & uncertainty in mapping processes [17][22]. Simple filtering techniques using average filtering & Kalman filtering have been implemented on the acquired data to go for comparative analysis of sensor performance & its impact on SLAM process towards reduction in uncertainty or minimal shift in landmark extraction [19].