MCVP: Multi-channel viewpoint planner for efficient exploration and mapping of complex 3D environments

Abstract

Autonomous exploration in complex 3D environments is a key issue in robotics, where current approaches often demonstrate limited efficiency and excessive path backtracking. To mitigate backtracking and repeated exploration in complex multi-channel environments, we propose MCVP (Multi-Channel Viewpoint Planner), an autonomous exploration strategy consisting of three key components: viewpoints generation, viewpoints optimization, and dual-resolution exploration path generation. Firstly, MCVP employs a mixed-cost heuristic function to generate high-quality viewpoints by integrating key factors, such as distance, yaw angle, and positional constraints. Subsequently, a viewpoints optimization process is applied to eliminate redundancies and enhance computational efficiency. To establish an efficient mapping between viewpoints and channels, a bidirectional hash table structure indexed by distance-based criteria is utilized, enabling rapid correspondence retrieval. Finally, the system generates a dual-resolution exploration path, enabling efficient and adaptive navigation for mobile robots in complex environments. We evaluate the proposed method against state-of-the-art approaches in multiple challenging simulation scenarios. The quantitative and qualitative results demonstrate that our method can successfully achieve complete exploration across diverse environments with high efficiency , while exhibiting significant advantages in terms of exploration time and movement distance. To further validate the proposed approach, we conduct real-world experiments in both an underground parking and a complex university campus. The experimental results also further confirm the robustness and practical feasibility of our method in realistic unknown environments.

Keywords

Autonomous exploration / Motion and path planning / Multi-channel environment / Viewpoint planner

CRediT authorship contribution statement

Qiang Zou: Writing – review & editing, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization. Jingyuan Liu: Writing – original draft, Visualization, Validation, Methodology, Investigation, Data curation, Conceptualization. Mengwen Lin: Writing – original draft, Methodology, Investigation, Data curation. Yu Wang: Visualization, Validation, Methodology. Fei Wang: Writing – review & editing, Supervision, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research was funded by the National Natural Science Foundation of China (62403110, 62373087), Guangdong Basic and Applied Basic Research Foundation (2023A1515110544), China Postdoctoral Science Foundation (2023M740541), Liaoning Revitalization Talents Program (XLYC24110114) and Fundamental Research Funds for the Central Universities (N25ZJL012).

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