This paper presents a self-supervised learning method to safely learn a
motion planner for ground robots to navigate environments with dense and
dynamic obstacles. When facing highly-cluttered, fast-moving, hard-to-predict
obstacles, classical motion planners may not be able to keep up with limited
onboard computation. For learning-based planners, high-quality demonstrations
are difficult to acquire for imitation learning while reinforcement learning
becomes inefficient due to the high probability of collision during
exploration. To safely and efficiently provide training data, the Learning from
Hallucination (LfH) approaches synthesize difficult navigation environments
based on past successful navigation experiences in relatively easy or
completely open ones, but unfortunately cannot address dynamic obstacles. In
our new Dynamic Learning from Learned Hallucination (Dyna-LfLH), we design and
learn a novel latent distribution and sample dynamic obstacles from it, so the
generated training data can be used to learn a motion planner to navigate in
dynamic environments. Dyna-LfLH is evaluated on a ground robot in both
simulated and physical environments and achieves up to 25% better success rate
compared to baselines.

本文提出了一种自我监督学习方法，用于安全学习地面机器人在密集和动态障碍物环境中导航的运动规划器。通过设计和学习新的潜在分布，并从中获取动态障碍物的样本，我们的动态学习方法在模拟和实际环境中得到了比基线方法高达 25% 的成功率。

Dyna-LfLH：从学习到幻影中学习动态环境中的敏捷导航

Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from  Learned Hallucination

Active visual SLAM finds a wide array of applications in GNSS-Denied
sub-terrain environments and outdoor environments for ground robots. To achieve
robust localization and mapping accuracy, it is imperative to incorporate the
perception considerations in the goal selection and path planning towards the
goal during an exploration mission. Through this work, we propose FIT-SLAM
(Fisher Information and Traversability estimation-based Active SLAM), a new
exploration method tailored for unmanned ground vehicles (UGVs) to explore 3D
environments. This approach is devised with the dual objectives of sustaining
an efficient exploration rate while optimizing SLAM accuracy. Initially, an
estimation of a global traversability map is conducted, which accounts for the
environmental constraints pertaining to traversability. Subsequently, we
propose a goal candidate selection approach along with a path planning method
towards this goal that takes into account the information provided by the
landmarks used by the SLAM backend to achieve robust localization and
successful path execution . The entire algorithm is tested and evaluated first
in a simulated 3D world, followed by a real-world environment and is compared
to pre-existing exploration methods. The results obtained during this
evaluation demonstrate a significant increase in the exploration rate while
effectively minimizing the localization covariance.

通过 FIT-SLAM 方法，该研究提出了一种新的 3D 环境探索方法，旨在提高无人地面车辆的探索速率并优化 SLAM 精度，该方法通过估计全局可通过性地图并结合 SLAM 后端使用的地标信息，实现了可靠的定位和路径规划。