Leveraging Pedestrian Detection and Tracking in Robotics Navigation: A Survey With Practical Illustrations

Pedestrian Detection and Tracking (PDT) plays a pivotal role in enabling autonomous robots to navigate safely and efficiently in dynamic, human-populated environments. This paper presents a comprehensive survey of PDT methods, structured according to the sensing modalities employed: RGB cameras, LiDAR, thermal imaging, RGB-D sensors, and multi-modal fusion systems. For each category, we analyze representative techniques, synthesize their strengths and limitations, and discuss recent advancements including deep learning approaches and cross-modal fusion strategies. We highlight persistent challenges such as handling occlusions, achieving real-time performance, and ensuring robustness across diverse environments. In addition to this structured review, we provide two practical examples using the Ona autonomous robot platform (see Figure 1) to illustrate how PDT techniques can enhance robotic capabilities in real-world scenarios. These examples focus on improving SLAM consistency and enabling proxemic-aware navigation strategies. Through this survey, we aim to clarify the current state of the art, identify emerging trends, and suggest future research directions for robust and socially-aware robotic navigation.