面向带重初始化的单目SLAM的尺度跳变感知位姿图松弛
Scale jump-aware pose graph relaxation for monocular SLAM with re-initializations
AI总结：
针对单目SLAM重初始化时帧间相对尺度未知的问题，提出结合常规相似性约束与尺度盲约束的混合位姿图松弛方法，可在多次重初始化下恢复全局一致轨迹。
摘要：
位姿图松弛已成为SLAM中不可或缺的技术，它能在满足成对相对变换约束的目标下，实现传感器参考坐标系的高效全局配准。这类约束可由增量运动估计或全局地点识别提供。虽然后者能实现回环检测与漂移补偿，但在单目场景下需格外注意：此时结构与位移的局部估计与真实值的差异不仅来自噪声，还来自尺度因子。由于尺度传播误差的累积，该尺度因子会随时间漂移，因此学界已提出尺度漂移感知的位姿图松弛方法。我们将这一思路拓展到后续传感器帧之间的相对尺度未知的情况——当单目SLAM进入重初始化、且无法识别出连续局部地图间的可靠重叠时，很容易出现这种状况。该方法通过混合位姿图公式实现，将常规的相似性一致性项与新型的尺度盲约束相结合。我们将该技术应用于具有实际意义的小型室内服务机器人场景，这类机器人可执行纯旋转位移，而这种情况极易导致跟踪失败。实验证明，即使沿回环发生多次重初始化，也能恢复全局一致的轨迹，同时我们还对成功与失败案例进行了深入研究。
英文摘要：
Pose graph relaxation has become an indispensable addition to SLAM enabling efficient global registration of sensor reference frames under the objective of satisfying pair-wise relative transformation constraints. The latter may be given by incremental motion estimation or global place recognition. While the latter case enables loop closures and drift compensation, care has to be taken in the monocular case in which local estimates of structure and displacements can differ from reality not just in terms of noise, but also in terms of a scale factor. Owing to the accumulation of scale propagation errors, this scale factor is drifting over time, hence scale-drift aware pose graph relaxation has been introduced. We extend this idea to cases in which the relative scale between subsequent sensor frames is unknown, a situation that can easily occur if monocular SLAM enters re-initialization and no reliable overlap between successive local maps can be identified. The approach is realized by a hybrid pose graph formulation that combines the regular similarity consistency terms with novel, scale-blind constraints. We apply the technique to the practically relevant case of small indoor service robots capable of effectuating purely rotational displacements, a condition that can easily cause tracking failures. We demonstrate that globally consistent trajectories can be recovered even if multiple re-initializations occur along the loop, and present an in-depth study of success and failure cases.
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。
