Advancing Robotic Automation in Biotechnology: Computer Vision Techniques for Simultaneous Localization and Mapping

Abstract

Simultaneous Localization and Mapping (SLAM), particularly using visual cues from computer vision, has become integral to the advancement of autonomous robotics within biotechnological environments. This study delves into visual SLAM (VSLAM) technologies, which are crucial for robots operating in unstructured or unknown environments typical in biotechnological applications. We explore a variety of VSLAM techniques structured around four main frameworks: Kalman filter-based, particle filter-based, expectation maximization-based, and set membership-based approaches. These methods are pivotal for enhancing robotic precision and efficiency in mapping and localization tasks under varied environmental conditions. Additionally, this paper examines the integration of VSLAM with advanced probabilistic and non-probabilistic approaches to address complex scenarios in robotic navigation and data assimilation. By presenting a comprehensive overview of current methodologies and their applications in the field, this paper highlights the transformative potential of VSLAM in supporting critical biotechnological operations, such as automated laboratory systems and intricate manufacturing processes. The practical implications of these advanced SLAM techniques are demonstrated through indoor experimental results, showcasing significant improvements in map accuracy and operational efficiency. This research underscores the ongoing evolution of computer vision and robotics, positioning VSLAM as a foundational technology for next-generation biotechnological advancements.