AMB3R-SLAM：具有分层后端的千米级SLAM
AMB3R-SLAM: Kilometer-scale SLAM with Hierarchical Backend
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中文总结 AI 辅助
AMB3R-SLAM是一种实时单目SLAM系统，采用分层后端在消费级GPU上重建千米级轨迹，避免静态世界假设，处理动态场景，并可将ATE降低超过70%。
中文摘要 AI 辅助
我们提出了AMB3R-SLAM，一个实时单目SLAM系统，能够在单个消费级GPU上重建超过10k帧的千米级轨迹。我们的模型将用于低延迟在线跟踪的轻量级前端与分层后端相结合，该后端逐步强制执行局部、中层和全局一致性。通过避免依赖静态世界假设的捆绑调整，我们的系统开箱即用地自然处理复杂的动态场景。此外，我们证明了我们的方法可以扩展以利用立体、RGB-D和LiDAR作为额外输入。AMB3R-SLAM在9个数据集上实现了强大的相机跟踪性能，在VBR和Oxford Spires上将先前最先进方法的绝对轨迹误差（ATE）降低了超过70%。通过额外的LiDAR输入，我们的模型在KITTI和VBR数据集上进一步将ATE降低到亚米级。
英文摘要
We present AMB3R-SLAM, a real-time monocular SLAM system capable of reconstructing kilometer-scale trajectories over 10k frames on a single consumer-grade GPU. Our model couples a lightweight front-end for low-latency online tracking with a hierarchical backend that progressively enforces local, mid-level, and global consistency. By avoiding bundle adjustment that relies on the static world assumption, our system naturally handles complex dynamic scenes out of the box. Furthermore, we demonstrate that our method can be extended to leverage stereo, RGB-D, and LiDAR as additional inputs. AMB3R-SLAM achieves strong camera tracking performance across 9 datasets, reducing the absolute trajectory error (ATE) of previous state-of-the-art methods on VBR and Oxford Spires by over 70%. With additional LiDAR input, our model further reduces ATE to sub-meter level on KITTI and VBR datasets.
发表机构
- University College London（伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。
