PerSeM is a training‑free framework that builds a persistent semantic memory for long‑horizon UAV mapping by associating frame‑wise segmentation results with world‑space voxels and refining them through spatial refinement, trust‑aware replay, and context‑guided verification. Experiments on Forest and UAVScenes benchmarks show that this persistent 3D memory improves semantic correctness and temporal stability compared to frame‑wise predictions, especially in semantically difficult and temporally unstable regions. The method achieves these gains without retraining or additional neural‑network inference.
By Saurbh Singh Jamwal, Ganesh Ramakrishnan
The paper introduces AeroBelief, a dual‑layer semantic‑spatial belief mapping framework for aerial object goal navigation. It separates broad contextual plausibility (intuition layer) from target‑specific evidence (evidence layer) and fuses them into persistent spatial belief hotspots. The method also employs object‑conditioned visual reasoning and egocentric regional guidance, achieving state‑of‑the‑art success rates on the UAV‑ON benchmark.
By Jianqiang Xiao, Xiang Deng, Yuexuan Sun, Yanjin Wu, Wenbiao Yan, Liqiang Nie
The paper introduces AeroBelief, a dual‑layer semantic‑spatial belief mapping framework for aerial object goal navigation. It separates broad contextual plausibility (intuition layer) from target‑specific evidence (evidence layer) and fuses them into persistent spatial belief hotspots, while also employing object‑conditioned visual reasoning and temporally stable regional guidance. Experiments on the UAV‑ON benchmark show AeroBelief outperforms prior methods in success rate, object success rate, and SPL.
arXiv:2605.25059v4 Announce Type: replace
Abstract: Crucial for autonomous exploration, online 3D occupancy prediction and mapping incrementally construct dense spatial representations on the fly. Em...
By Ruoyu Wang, Yong Liu, Jiahan Li, Sheng Tao, Yuhang Lin, Yukai Ma
VoxelFix is a graph‑based post‑hoc semantic correction method that refines voxel labels in completed 3D voxel maps while preserving their geometry and occupancy. It learns to correct errors by exploiting local geometry and neighboring semantic information, using training pairs generated by corrupting annotated maps with class confusions from upstream perception pipelines. Experiments on OccuFly maps show consistent improvements of 4.23–5.00 percentage points in mIoU, especially for tree, roof, and wall classes, and the method generalizes to out‑of‑distribution aerial scenes.
By Sunesh Praveen Raja Sundarasami, Taehyoung Kim, Johannes Scherer, Toma\v{z} Coti\v{c}, Sivasubiramaniam Subbiah, Andreas Greiner, Paul Spannaus, Sebastian Houben
arXiv:2601.08355v3 Announce Type: replace
Abstract: Vision-Language Models (VLMs) are increasingly deployed in autonomous driving and embodied AI systems, where reliable perception is critical for sa...
By Guo Cheng, Huang Li