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

We introduce a new system for Multi-Session SLAM, which tracks camera motion
across multiple disjoint videos under a single global reference. Our approach
couples the prediction of optical flow with solver layers to estimate camera
pose. The backbone is trained end-to-end using a novel differentiable solver
for wide-baseline two-view pose. The full system can connect disjoint
sequences, perform visual odometry, and global optimization. Compared to
existing approaches, our design is accurate and robust to catastrophic
failures. Code is available at github.com/princeton-vl/MultiSlam_DiffPose

我们介绍了一种新的多会话 SLAM 系统，可以在一个全局参考下跟踪多个不同的视频中的相机运动。我们的方法通过预测光流，并结合求解器层来估计相机姿态。该系统可以连接不连续的序列，进行视觉测距和全局优化，与现有方法相比，我们的设计精确且对灾难性故障具有强韧性。代码可在 github.com/princeton-vl/MultiSlam_DiffPose 上找到。