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By combining learned guidance with safety-constrained optimization, SaferPath enables robots to navigate complex, cluttered environments with significantly improved safety and success rates compared to end-to-end methods.
Achieve state-of-the-art open-vocabulary occupancy prediction in complex indoor scenes by using only binary occupancy labels and a novel Gaussian-based representation.
By extending surface points inward along camera rays and representing volumetric samples as Gaussian primitives, GPOcc achieves state-of-the-art results in monocular and streaming occupancy prediction, outperforming existing methods by a significant margin while also running faster.
Achieve robust kilometer-scale SLAM in dynamic thermal environments by combining learned thermal features with geometric and semantic constraints, outperforming state-of-the-art visual SLAM methods.
