用生成模型提升机器人探索中的地图质量与导航能力
Robust Robotic Exploration and Mapping Using Generative Occupancy Map Synthesis
- 基于扩散模型的3D占位图生成，从部分观测推断完整场景
- 实测地图质量提升24.44%（近距）和75.59%（远距）FID
- 可直接接入现有规划器，显著增强探索鲁棒性与路径效率
我们提出一种新方法，通过生成式占位图合成提升机器人探索能力。实现SceneSense——一个专为从局部观测预测3D占位图而设计并训练的扩散模型。该方法在实时中将生成预测概率融合进动态占位图，显著提升地图质量与可通行性。我们在四足机器人上部署SceneSense，并通过真实世界实验验证其有效性：与完全观测的真值数据相比，增强后的占位图在机器人附近实现24.44%的FID改进，在远距离达75.59%改进。此外，将SceneSense增强的地图作为“即插即用”模块集成至现有探索系统，仅使用现成规划器即实现鲁棒性与可通行时间的提升。最后，在两种不同环境中的全探索评估显示，局部增强地图比仅依赖传感器测量的地图更具一致性。
原文摘要 · Abstract (English)
We present a novel approach for enhancing robotic exploration by using generative occupancy mapping. We implement SceneSense, a diffusion model designed and trained for predicting 3D occupancy maps given partial observations. Our proposed approach probabilistically fuses these predictions into a running occupancy map in real-time, resulting in significant improvements in map quality and traversability. We deploy SceneSense on a quadruped robot and validate its performance with real-world experiments to demonstrate the effectiveness of the model. In these experiments we show that occupancy maps enhanced with SceneSense predictions better estimate the distribution of our fully observed ground truth data ($24.44\%$ FID improvement around the robot and $75.59\%$ improvement at range). We additionally show that integrating SceneSense enhanced maps into our robotic exploration stack as a ``drop-in'' map improvement, utilizing an existing off-the-shelf planner, results in improvements in robustness and traversability time. Finally, we show results of full exploration evaluations with our proposed system in two dissimilar environments and find that locally enhanced maps provide more consistent exploration results than maps constructed only from direct sensor measurements.
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