Autonomous navigation at high speeds in off-road environments necessitates
robots to comprehensively understand their surroundings using onboard sensing
only. The extreme conditions posed by the off-road setting can cause degraded
camera image quality due to poor lighting and motion blur, as well as limited
sparse geometric information available from LiDAR sensing when driving at high
speeds. In this work, we present RoadRunner, a novel framework capable of
predicting terrain traversability and an elevation map directly from camera and
LiDAR sensor inputs. RoadRunner enables reliable autonomous navigation, by
fusing sensory information, handling of uncertainty, and generation of
contextually informed predictions about the geometry and traversability of the
terrain while operating at low latency. In contrast to existing methods relying
on classifying handcrafted semantic classes and using heuristics to predict
traversability costs, our method is trained end-to-end in a self-supervised
fashion. The RoadRunner network architecture builds upon popular sensor fusion
network architectures from the autonomous driving domain, which embed LiDAR and
camera information into a common Bird's Eye View perspective. Training is
enabled by utilizing an existing traversability estimation stack to generate
training data in hindsight in a scalable manner from real-world off-road
driving datasets. Furthermore, RoadRunner improves the system latency by a
factor of roughly 4, from 500 ms to 140 ms, while improving the accuracy for
traversability costs and elevation map predictions. We demonstrate the
effectiveness of RoadRunner in enabling safe and reliable off-road navigation
at high speeds in multiple real-world driving scenarios through unstructured
desert environments.

在本研究中，我们提出了一种名为 RoadRunner 的新框架，该框架能够从相机和 LiDAR 传感器输入中直接预测地形可通行性和高程图，通过融合传感器信息、处理不确定性和生成具有上下文信息的预测，实现可靠的自主导航，并减少系统延迟并提高可靠性。

RoadRunner -- 自主越野行驶的可行性估计学习

RoadRunner -- Learning Traversability Estimation for Autonomous Off-road  Driving

Autonomous navigation in off-road conditions requires an accurate estimation
of terrain traversability. However, traversability estimation in unstructured
environments is subject to high uncertainty due to the variability of numerous
factors that influence vehicle-terrain interaction. Consequently, it is
challenging to obtain a generalizable model that can accurately predict
traversability in a variety of environments. This paper presents METAVerse, a
meta-learning framework for learning a global model that accurately and
reliably predicts terrain traversability across diverse environments. We train
the traversability prediction network to generate a dense and continuous-valued
cost map from a sparse LiDAR point cloud, leveraging vehicle-terrain
interaction feedback in a self-supervised manner. Meta-learning is utilized to
train a global model with driving data collected from multiple environments,
effectively minimizing estimation uncertainty. During deployment, online
adaptation is performed to rapidly adapt the network to the local environment
by exploiting recent interaction experiences. To conduct a comprehensive
evaluation, we collect driving data from various terrains and demonstrate that
our method can obtain a global model that minimizes uncertainty. Moreover, by
integrating our model with a model predictive controller, we demonstrate that
the reduced uncertainty results in safe and stable navigation in unstructured
and unknown terrains.

METAVerse 是一个元学习框架，通过利用多环境驾驶数据训练一个全局模型，从稀疏的 LiDAR 点云中生成稠密连续值的成本地图来准确可靠地预测不同环境下的地形可通行性，结合模型预测控制器，实现了在结构化和未知地形上安全稳定的导航。