论文标题
通过SPD（3）歧管和提高的成本功能，使对象具有里程碑标记的参数化和约束
Making Parameterization and Constrains of Object Landmark Globally Consistent via SPD(3) Manifold and Improved Cost Functions
论文作者
论文摘要
对象级别的SLAM引入语义有意义，紧凑的对象标志，以帮助室内机器人应用程序和室外自主驾驶任务。但是，对象级猛击的后端遇到了奇异性问题，因为现有方法通过其量表和姿势分别参数对象标记。在该参数化方法下，可以通过将对象坐标帧旋转90度并以宽度值换成相同的抽象对象，从而使同一对象标志的姿势在全球范围内保持一致。为了避免奇异性问题，我们首先引入了对称的正数（SPD）矩阵歧管，作为改进的对象级地标表示，并进一步改善了后端的成本函数，以使其与表示形式兼容。我们的方法表明，在模拟实验中，收敛速度更快和鲁棒性。实际数据集的实验还表明，使用相同的前端数据，我们的策略平均提高了映射准确性22％。
Object-level SLAM introduces semantic meaningful and compact object landmarks that help both indoor robot applications and outdoor autonomous driving tasks. However, the back end of object-level SLAM suffers from singularity problems because existing methods parameterize object landmark separately by their scales and poses. Under that parameterization method, the same abstract object can be represented by rotating the object coordinate frame by 90 deg and swapping its length with width value, making the pose of the same object landmark not globally consistent. To avoid the singularity problem, we first introduce the symmetric positive-definite (SPD) matrix manifold as an improved object-level landmark representation and further improve the cost functions in the back end to make them compatible with the representation. Our method demonstrates a faster convergence rate and more robustness in simulation experiments. Experiments on real datasets also reveal that using the same front-end data, our strategy improves the mapping accuracy by 22% on average.
