StepNav: Structured Trajectory Priors for Efficient and Multimodal Visual Navigation

Visual navigation is fundamental to autonomous systems, yet generating reliable trajectories in cluttered and uncertain environments remains a core challenge. Recent generative models promise end-to-end synthesis, but their reliance on unstructured noise priors often yields unsafe, inefficient, or unimodal plans that cannot meet real-time requirements. We propose StepNav, a novel framework that bridges this gap by introducing structured, multimodal trajectory priors derived from variational principles. StepNav first learns a geometry-aware success probability field to identify all feasible navigation corridors. These corridors are then used to construct an explicit, multi-modal mixture prior that initializes a conditional flow-matching process. This refinement is formulated as an optimal control problem with explicit smoothness and safety regularization. By replacing unstructured noise with physically-grounded candidates, StepNav generates safer and more efficient plans in significantly fewer steps. Experiments in both simulation and real-world benchmarks demonstrate consistent improvements in robustness, efficiency, and safety over state-of-the-art generative planners, advancing reliable trajectory generation for practical autonomous navigation. The code has been released at https://github.com/LuoXubo/StepNav.

LuSNAR:A Lunar Segmentation, Navigation and Reconstruction Dataset based on Muti-sensor for Autonomous Exploration

With the complexity of lunar exploration missions, the moon needs to have a higher level of autonomy. Environmental perception and navigation algorithms are the foundation for lunar rovers to achieve autonomous exploration. The development and verification of algorithms require highly reliable data support. Most of the existing lunar datasets are targeted at a single task, lacking diverse scenes and high-precision ground truth labels. To address this issue, we propose a multi-task, multi-scene, and multi-label lunar benchmark dataset LuSNAR. This dataset can be used for comprehensive evaluation of autonomous perception and navigation systems, including high-resolution stereo image pairs, panoramic semantic labels, dense depth maps, LiDAR point clouds, and the position of rover. In order to provide richer scene data, we built 9 lunar simulation scenes based on Unreal Engine. Each scene is divided according to topographic relief and the density of objects. To verify the usability of the dataset, we evaluated and analyzed the algorithms of semantic segmentation, 3D reconstruction, and autonomous navigation. The experiment results prove that the dataset proposed in this paper can be used for ground verification of tasks such as autonomous environment perception and navigation, and provides a lunar benchmark dataset for testing the accessibility of algorithm metrics. We make LuSNAR publicly available at: https://github.com/autumn999999/LuSNAR-dataset.