条条大路通罗马：面向开放环境主动三维建图的流驱动多锚点探索

中文摘要 AI 辅助

为推进具身智能的发展，开放环境主动三维建图日益受到关注，其目标是在未知场景中执行长时域、最短轨迹的探索以进行重建。由于关于未知环境的信息有限，基于封闭集假设（即假设测试环境与训练时所见环境相似）的方法难以实现令人满意的泛化。在现有的主动建图方法中，长时域探索通常通过预测一个粗略的远距离目标，并将其转化为可执行路径来引导。然而，这一阶段通常被表述为单点预测。在部分可观测条件下，相同的局部观测可能对应多个合理的探索方向，使得这种确定性预测容易导致决策脆弱，并在未知场景中性能下降。我们的实验进一步验证了这是其泛化能力弱的关键因素。为解决此问题，我们将长时域目标预测重新表述为使用条件流匹配的条件多模态锚点生成。与预测单一目标不同，我们的方法从当前建图状态学习关于粗略探索锚点的条件分布。这些锚点首先通过考虑障碍物的规划转化为可执行的候选路径。然后，我们应用探索模式聚类来压缩几何上相似的轨迹并减少候选冗余。最后，一个分层选择模块选择最有希望的模式，并在其中对路径重新排序，以生成最终可执行的轨迹。实验表明，我们的方法提高了开放环境中的泛化能力和重建效率。

英文摘要

To advance the development of embodied intelligence, Open-Environment Active 3D Mapping has attracted increasing attention, aiming to perform a long-horizon and shortest trajectory exploration for reconstructing unseen scenarios. Since only limited information about unseen environments is available, methods built on the closed-set assumption, i.e., assuming that the test environments are similar to those seen during training, cannot generalize satisfactorily. In existing active mapping methods, long-horizon exploration is often guided by predicting a coarse long-range goal and then converting it into an executable path. However, this stage is usually formulated as single-point prediction. Under partial observability, the same local observation may correspond to multiple plausible exploration directions, making such deterministic prediction prone to brittle decisions and degraded performance in unseen scenarios. Our experiments further verify that this is a key factor underlying their weak generalization. To address this issue, we reformulate long-horizon target prediction as conditional multimodal anchor generation using Conditional Flow Matching.Instead of predicting a single goal, our method learns a conditional distribution over coarse exploration anchors from the current mapping state. These anchors are first converted into executable candidate paths through obstacle-aware planning. We then apply exploration-mode clustering to compress geometrically similar trajectories and reduce candidate redundancy. Finally, a hierarchical selection module selects the most promising mode and reranks paths within it to produce the final executable trajectory. Experiments show that our method improves generalization and reconstruction efficiency in open environments.

发表机构

• School of Artificial Intelligence, Tianjin University（天津大学人工智能学院）
• School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）

机构由 AI 辅助整理，请以论文原文为准。

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Accepted by NeurIPS 2026