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, LiD...
| Authors: | , , , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2025 |
| Country: | España |
| Institution: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repository: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:dnet:digitalcsic_::3b4045f5c993c93655819990c658da8c |
| Online Access: | http://hdl.handle.net/10261/427820 https://api.elsevier.com/content/abstract/scopus_id/105017171028 |
| Access Level: | Open access |
| Keyword: | Human-aware navigation Multi-sensor fusion Pedestrian detection People tracking Robot navigation |
| Summary: | 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 ) 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. |
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