TACO：用于连续神经映射的时间共识优化
TACO: Temporal Consensus Optimization for Continual Neural Mapping
AI总结：
提出TACO框架，通过将映射建模为时间共识优化问题，无需回放历史数据即可实现动态环境下的连续神经映射，平衡了内存效率与适应性。
摘要：
神经隐式映射已成为机器人导航和场景理解的有力范式。然而，实际机器人部署需要在严格的内存和计算约束下持续适应变化的环境，现有映射系统无法支持这一点。大多数先前方法依赖于回放历史观测来保持一致性，并假设静态场景。因此，它们无法适应动态机器人环境中的连续学习。为应对这些挑战，我们提出TACO（时间共识优化），一种无回放的连续神经映射框架。我们将映射重新表述为时间共识优化问题，其中将过去的模型快照视为时间邻居。直观上，我们的方法类似于模型咨询其自身的过去知识。我们通过强制与历史表示进行加权共识来更新当前地图。我们的方法允许可靠的历史几何约束优化，同时允许不可靠或过时的区域根据新观测进行修订。TACO在无需存储或回放先前数据的情况下实现了内存效率与适应性之间的平衡。通过广泛的模拟和真实世界实验，我们表明TACO能够稳健地适应场景变化，并持续优于其他连续学习基线。代码可在 https://iconlab.negarmehr.com/TACO 获取。
英文摘要：
Neural implicit mapping has emerged as a powerful paradigm for robotic navigation and scene understanding. However, real-world robotic deployment requires continual adaptation to changing environments under strict memory and computation constraints, which existing mapping systems fail to support. Most prior methods rely on replaying historical observations to preserve consistency and assume static scenes. As a result, they cannot adapt to continual learning in dynamic robotic settings. To address these challenges, we propose TACO (TemporAl Consensus Optimization), a replay-free framework for continual neural mapping. We reformulate mapping as a temporal consensus optimization problem, where we treat past model snapshots as temporal neighbors. Intuitively, our approach resembles a model consulting its own past knowledge. We update the current map by enforcing weighted consensus with historical representations. Our method allows reliable past geometry to constrain optimization while permitting unreliable or outdated regions to be revised in response to new observations. TACO achieves a balance between memory efficiency and adaptability without storing or replaying previous data. Through extensive simulated and real-world experiments, we show that TACO robustly adapts to scene changes, and consistently outperforms other continual learning baselines. Code is available at https://iconlab.negarmehr.com/TACO
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
