Loop Closure Detection is a fundamental component of any SLAM system. By performing place recognition, a robot can correct accumulated drift errors arising from odometry uncertainties during the mapping process. Numerous techniques have been proposed in the literature to address this task under different sensor configurations, including RGB cameras and LiDAR. Among these, LiDAR-based SLAM has gained substantial attention due to its robustness in outdoor environments and its invariance to illumination changes. However, LiDAR sensors inherently provide less texture information compared to cameras, introducing additional challenges for loop closure detection. One of the most widely adopted approaches in LiDAR-based SLAM is Scan Context, recognized for its simplicity and effectiveness. This method has been successfully integrated into a broad range of applications and algorithms. Nevertheless, its simplicity can also lead to reduced robustness when no supplementary verification mechanisms are employed. In this work, we introduce Gaussian Scan Context, an enhancement to the original Scan Context that incorporates statistical analysis of the input point cloud. This approach accounts for the distribution of points within each context bin. Experimental results demonstrate that this enhancement improves both robustness and overall performance, as supported by various metrics.
The transition of autonomous mobile robots from controlled industrial settings to dynamic, human-centric environments, such as manufacturing, logistics, and healthcare, has made their safe and autonomous operation a critical area of research. These sophisticated machines must be capable of perceiving, understanding, and interacting with their surroundings to navigate freely and perform complex tasks. A significant obstacle to achieving this is the lack of comprehensive contextual awareness, which requires a robot to recognize its spatial environment and identify the objects and actors within it. Without this perceptual knowledge, robots struggle to plan adaptive behaviors or engage in meaningful interaction with humans. This thesis presents novel solutions to this challenge by exploring two distinct but complementary research directions. The first direction involves human re-identification and tracking to improve Human-Robot Collaboration. Our developed approach enables a mobile robot to recognize a specific person, facilitating targeted collaboration while ignoring other individuals. The second direction focuses on enhancing the robot's overall perceptual capabilities to understand its environment geometrically and semantically. Geometric information is vital for motion planning and collision avoidance, while semantic knowledge provides the robot with a richer understanding for more advanced interaction. Both solutions are driven by the improvement of the semantical understanding of robots that enhance their knowledge of their surroundings, allowing a smoother and more natural interaction between robots, humans, and the environment. The contributions of this work in human re-identification and environmental understanding represent a significant step toward a future where robots are more contextually aware, enabling safer coexistence and more effective collaboration.
