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.

翻译：位姿图松弛已成为SLAM不可或缺的补充，通过满足成对相对变换约束的目标，实现传感器参考系的高效全局配准。这些约束可由增量运动估计或全局位置识别提供。全局位置识别可支持闭环检测与漂移补偿，但在单目情况下需特别谨慎：局部结构与位移估计不仅存在噪声误差，还可能存在尺度因子偏差。由于尺度传播误差的累积，该尺度因子随时间漂移，因此引入了尺度漂移感知的位姿图松弛方法。我们将该思想扩展到后续传感器帧间相对尺度未知的场景——当单目SLAM进入重新初始化且无法识别连续局部地图间的可靠重叠时，此类情况极易发生。该方法通过混合位姿图公式实现，将常规相似性一致性项与新型尺度盲约束相结合。我们将该技术应用于小型室内服务机器人这一实际场景，此类机器人能够执行纯旋转位移（易导致跟踪失败）。实验证明，即使沿闭环路径发生多次重新初始化，仍可恢复全局一致的轨迹，并对成功与失败案例进行了深入研究。