The principal objective of autonomous navigation involves terrain traversability analysis,where traversability refers to the suitability of a given terrain for driving over.It is difficult to infer the traversability ...The principal objective of autonomous navigation involves terrain traversability analysis,where traversability refers to the suitability of a given terrain for driving over.It is difficult to infer the traversability cost from the semantic types or geometric properties of the terrain independently.Robots may develop a false perception of high grass and rugged dirt.To address these challenges,this paper proposes a method for local traversability map generation,which uses onboard LiDAR and cameras to generate local traversability maps.One of the key ideas to achieve this is to build the interaction between the geometric properties and the types of terrain.This relationship represents the sensitivity of traversability to geometry under certain types of terrain,and it will be used in conjunction with semantics and geometry to reason about traversability.Further,to prevent side or longitudinal slipping that exceeds the capacity of the traffic system,vehicle classes and design factors are also incorporated into the calculation of traversability costs.Real-world experimental results demonstrate that the proposed method can generate traversability maps in unstructured environments.Ablation studies substantiate the method's efficacy.Compared to existing methods,our approach provides more reasonable analysis results when dealing with complex environments featuring diverse terrains.展开更多
基金supported by National Natural Science Foundations of China(Grant No.52205047 and No.52175037)China postdoctoral Science Foundations(Grant No.BX20220379 and No.2021M700422)Frontier Cross Project(2024CX11006).
文摘The principal objective of autonomous navigation involves terrain traversability analysis,where traversability refers to the suitability of a given terrain for driving over.It is difficult to infer the traversability cost from the semantic types or geometric properties of the terrain independently.Robots may develop a false perception of high grass and rugged dirt.To address these challenges,this paper proposes a method for local traversability map generation,which uses onboard LiDAR and cameras to generate local traversability maps.One of the key ideas to achieve this is to build the interaction between the geometric properties and the types of terrain.This relationship represents the sensitivity of traversability to geometry under certain types of terrain,and it will be used in conjunction with semantics and geometry to reason about traversability.Further,to prevent side or longitudinal slipping that exceeds the capacity of the traffic system,vehicle classes and design factors are also incorporated into the calculation of traversability costs.Real-world experimental results demonstrate that the proposed method can generate traversability maps in unstructured environments.Ablation studies substantiate the method's efficacy.Compared to existing methods,our approach provides more reasonable analysis results when dealing with complex environments featuring diverse terrains.
