论文标题
双峰：强大的单相机导航框架
Dual-SLAM: A framework for robust single camera navigation
论文作者
论文摘要
SLAM（同时本地化和映射）试图提供实时自定位的移动代理。为了达到实时速度，猛击逐步传播位置估计值。这使得大满贯，但也使其容易受到本地姿势估计失败的影响。由于当地姿势估计不足，因此定期进行局部姿势估计失败，这使整体大满贯系统变得脆弱。本文试图纠正此问题。我们注意到，虽然局部姿势估计不足，但对较长序列的姿势估计得到了很好的条件。因此，局部姿势估计错误最终表现为映射不一致。发生这种情况时，我们保存当前地图并激活两个新的大满贯线程。一个处理传入的帧以创建新的地图，另一个恢复线程，回溯，将新的和旧的地图链接在一起。这创建了一个双链接框架，该框架保持实时性能，同时对本地姿势估计失败进行稳健。在基准数据集上的评估表明，双峰会可以将失败降低$ 88 \％$。
SLAM (Simultaneous Localization And Mapping) seeks to provide a moving agent with real-time self-localization. To achieve real-time speed, SLAM incrementally propagates position estimates. This makes SLAM fast but also makes it vulnerable to local pose estimation failures. As local pose estimation is ill-conditioned, local pose estimation failures happen regularly, making the overall SLAM system brittle. This paper attempts to correct this problem. We note that while local pose estimation is ill-conditioned, pose estimation over longer sequences is well-conditioned. Thus, local pose estimation errors eventually manifest themselves as mapping inconsistencies. When this occurs, we save the current map and activate two new SLAM threads. One processes incoming frames to create a new map and the other, recovery thread, backtracks to link new and old maps together. This creates a Dual-SLAM framework that maintains real-time performance while being robust to local pose estimation failures. Evaluation on benchmark datasets shows Dual-SLAM can reduce failures by a dramatic $88\%$.
