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 Our method allows reliable past geometry to constrain optimization while permitting unreliable or outdated regions to be revised in response to new observations.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.

翻译：神经隐式建图已成为机器人导航与场景理解的重要范式。然而，实际机器人部署需要在严格的内存与计算限制下持续适应动态变化的环境，现有建图系统均无法满足这一需求。多数现有方法依赖回放历史观测以保持一致性，并假设场景静态不变，因而无法适应动态机器人场景中的持续学习。为应对这些挑战，我们提出TACO（时间一致性优化），一种无需回放的持续神经建图框架。我们将建图重新定义为时间一致性优化问题，将历史模型快照视为时间邻域。直观而言，该方法类似于模型咨询其自身的历史知识。我们通过强制当前地图与历史表征达成加权一致性来实现地图更新。该方法允许可靠的过往几何结构约束优化过程，同时容许根据新观测修正不可靠或过时的区域。TACO在不存储或回放先前数据的前提下，实现了内存效率与适应性的平衡。通过大量仿真与真实世界实验，我们证明TACO能够稳健适应场景变化，并持续优于其他持续学习基线方法。