Despite the remarkable advancements in deep learning-based perception
technologies and simultaneous localization and mapping~(SLAM), one can face the
failure of these approaches when robots encounter scenarios outside their
modeled experiences~(here, the term \textit{modeling} encompasses both
conventional pattern finding and data-driven approaches). In particular,
because learning-based methods are prone to catastrophic failure when operated
in untrained scenes, there is still a demand for conventional yet robust
approaches that work out of the box in diverse scenarios, such as real-world
robotic services and SLAM competitions. In addition, the dynamic nature of
real-world environments, characterized by changing surroundings over time and
the presence of moving objects, leads to undesirable data points that hinder a
robot from localization and path planning. Consequently, methodologies that
enable long-term map management, such as multi-session SLAM and static map
building, become essential. Therefore, to achieve a robust long-term robotic
mapping system that can work out of the box, first, I propose (i)~fast and
robust ground segmentation to reject the ground points, which are featureless
and thus not helpful for localization and mapping. Then, by employing the
concept of graduated non-convexity~(GNC), I propose (ii)~outlier-robust
registration with ground segmentation that overcomes the presence of gross
outliers within the feature matching results, and (iii)~hierarchical
multi-session SLAM that not only uses our proposed GNC-based registration but
also employs a GNC solver to be robust against outlier loop candidates.
Finally, I propose (iv)~instance-aware static map building that can handle the
presence of moving objects in the environment based on the observation that
most moving objects in urban environments are inevitably in contact with the
ground.

通过提出快速而稳定的地面分割、鲁棒的带地面分割的异常值鲁棒注册、层次化多场景 SLAM 以及基于实例感知的静态地图构建，实现了一个长期稳定的机器人建图系统。

利用地面分割实现抗离群点的长期机器人地图构建

Outlier-Robust Long-Term Robotic Mapping Leveraging Ground Segmentation

With the rapid development of LLMs, it is natural to ask how to harness their
capabilities efficiently. In this paper, we explore whether it is feasible to
direct each input query to a single most suitable LLM. To this end, we propose
LLM routing for challenging reasoning tasks. Our extensive experiments suggest
that such routing shows promise but is not feasible in all scenarios, so more
robust approaches should be investigated to fill this gap.

我们探讨了是否可以将每个输入查询直接路由到最合适的 LLM，我们的广泛实验表明这样的路由有前景但在所有情景下都不可行，因此应该研究更加稳健的方法来填补这一空白。