Autonomous micro aerial vehicles still struggle with fast and agile
maneuvers, dynamic environments, imperfect sensing, and state estimation drift.
Autonomous drone racing brings these challenges to the fore. Human pilots can
fly a previously unseen track after a handful of practice runs. In contrast,
state-of-the-art autonomous navigation algorithms require either a precise
metric map of the environment or a large amount of training data collected in
the track of interest. To bridge this gap, we propose an approach that can fly
a new track in a previously unseen environment without a precise map or
expensive data collection. Our approach represents the global track layout with
coarse gate locations, which can be easily estimated from a single
demonstration flight. At test time, a convolutional network predicts the poses
of the closest gates along with their uncertainty. These predictions are
incorporated by an extended Kalman filter to maintain optimal
maximum-a-posteriori estimates of gate locations. This allows the framework to
cope with misleading high-variance estimates that could stem from poor
observability or lack of visible gates. Given the estimated gate poses, we use
model predictive control to quickly and accurately navigate through the track.
We conduct extensive experiments in the physical world, demonstrating agile and
robust flight through complex and diverse previously-unseen race tracks. The
presented approach was used to win the IROS 2018 Autonomous Drone Race
Competition, outracing the second-placing team by a factor of two.

本研究提出了一种基于全局轨迹布局及卷积网络预测与卡尔曼滤波融合的方法，重点解决自主微型飞行器的难以精确的导航、环境不确定以及姿态估计误差等问题，成功应用于 IROS2018 无人机比赛中并取得优异成绩。

美女与野兽：最优方法与学习相遇于无人机竞速

Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing

While effective navigation in large, crowded environments is essential for an
autonomous robot, preliminary testing of algorithms to support it requires
simulation across a broad range of crowd scenarios. Most available simulation
tools provide either realistic crowds without robots or realistic robots
without realistic crowds. This paper introduces MengeROS, a 2-D simulator that
realistically integrates multiple robots and crowds. MengeROS provides a broad
range of settings in which to test the capabilities and performance of
navigation algorithms designed for large crowded environments.

本文介绍了一种 2D 模拟器 MengeROS，该模拟器可在多个环境中模拟多个机器人和人群，为测试适用于大型拥挤环境的导航算法的能力和性能提供广泛的设置。