Loop closure can effectively correct the accumulated error in robot localization, which plays a critical role in the long-term navigation of the robot. Traditional appearance-based methods rely on local features and are prone to failure in ambiguous environments. On the other hand, object recognition can infer objects' category, pose, and extent. These objects can serve as stable semantic landmarks for viewpoint-independent and non-ambiguous loop closure. However, there is a critical object-level data association problem due to the lack of efficient and robust algorithms. We introduce a novel object-level data association algorithm, which incorporates IoU, instance-level embedding, and detection uncertainty, formulated as a linear assignment problem. Then, we model the objects as TSDF volumes and represent the environment as a 3D graph with semantics and topology. Next, we propose a graph matching-based loop detection based on the reconstructed 3D semantic graphs and correct the accumulated error by aligning the matched objects. Finally, we refine the object poses and camera trajectory in an object-level pose graph optimization. Experimental results show that the proposed object-level data association method significantly outperforms the commonly used nearest-neighbor method in accuracy. Our graph matching-based loop closure is more robust to environmental appearance changes than existing appearance-based methods.
翻译:环关闭可以有效纠正机器人本地化中累积的错误。 机器人本地化在机器人的长期导航中起着关键作用。 传统的外观方法依赖于本地特征, 容易在模糊环境中发生故障。 另一方面, 对象识别可以推断对象的类别、 表面和范围。 这些对象可以作为视觉独立和不矛盾的环关闭的稳定的语义标志。 但是, 由于缺乏高效和稳健的算法, 存在一个关键的目标级数据关联问题 。 我们引入了一个新的对象级数据关联算法, 其中包括IoU、 实例级嵌入和检测不确定性, 并将其作为线性任务问题来制定。 然后, 我们用 TCDF 量来模拟对象, 并将环境代表环境为 3D 图表 。 接下来, 我们建议以重建后的 3D 语义图为基础进行基于图形的匹配性循环检测, 并通过对匹配对象级图像优化来纠正累积的错误 。 最后, 我们将对象级数据配置和摄像轨迹的轨迹轨迹进行精细化, 实验结果显示, 拟议的对象级数据组合比我们目前使用的最接近的图像显示的图像显示方法。