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.
翻译:对象级 SLAM 引入了有助于室内机器人应用和户外自主驾驶任务的语义有意义和紧凑物体标志。 但是, 目标级 SLAM 的后端存在奇特性问题, 因为现有方法将天体标志按其大小和外形分别参数化。 根据这种参数化方法, 相同的抽象对象可以通过用90度旋转天体坐标框架, 将其长度与宽度值互换, 使同一天体标志的外形不具有全球一致性。 为了避免独一性问题, 我们首先引入了对称正定矩阵, 作为改进的天体级标志性代表, 并进一步改进后端的成本功能, 使其与代表相容。 我们的方法显示在模拟实验中更快的趋同率和更加稳健性。 对真实数据集的实验还显示, 使用相同的前端数据, 我们的战略平均将绘图精确度提高22% 。