We study pursuit-evasion games in highly occluded urban environments, e.g.
tall buildings in a city, where a scout (quadrotor) tracks multiple dynamic
targets on the ground. We show that we can build a neural radiance field (NeRF)
representation of the city -- online -- using RGB and depth images from
different vantage points. This representation is used to calculate the
information gain to both explore unknown parts of the city and track the
targets -- thereby giving a completely first-principles approach to actively
tracking dynamic targets. We demonstrate, using a custom-built simulator using
Open Street Maps data of Philadelphia and New York City, that we can explore
and locate 20 stationary targets within 300 steps. This is slower than a greedy
baseline which which does not use active perception. But for dynamic targets
that actively hide behind occlusions, we show that our approach maintains, at
worst, a tracking error of 200m; the greedy baseline can have a tracking error
as large as 600m. We observe a number of interesting properties in the scout's
policies, e.g., it switches its attention to track a different target
periodically, as the quality of the NeRF representation improves over time, the
scout also becomes better in terms of target tracking.

通过在高度遮挡的城市环境中研究追逐与逃避游戏，我们可以使用神经辐射场（NeRF）来实时构建城市的表示，并以完全的先验方式积极跟踪动态目标。我们的方法在追踪动态目标方面表现较优，且观察到侦察兵的策略具有一些有趣的特性，例如随着时间的推移，NeRF 表示的质量提高，侦察兵也在目标跟踪方面变得更好。

活动侦察员：在密集城市环境中使用神经辐射场进行多目标跟踪

Active Scout: Multi-Target Tracking Using Neural Radiance Fields in  Dense Urban Environments

Pursuit-evasion games (PEGs) model interactions between a team of pursuers
and an evader in graph-based environments such as urban street networks. Recent
advancements have demonstrated the effectiveness of the pre-training and
fine-tuning paradigm in PSRO to improve scalability in solving large-scale
PEGs. However, these methods primarily focus on specific PEGs with fixed
initial conditions that may vary substantially in real-world scenarios, which
significantly hinders the applicability of the traditional methods. To address
this issue, we introduce Grasper, a GeneRAlist purSuer for Pursuit-Evasion
pRoblems, capable of efficiently generating pursuer policies tailored to
specific PEGs. Our contributions are threefold: First, we present a novel
architecture that offers high-quality solutions for diverse PEGs, comprising
critical components such as (i) a graph neural network (GNN) to encode PEGs
into hidden vectors, and (ii) a hypernetwork to generate pursuer policies based
on these hidden vectors. As a second contribution, we develop an efficient
three-stage training method involving (i) a pre-pretraining stage for learning
robust PEG representations through self-supervised graph learning techniques
like GraphMAE, (ii) a pre-training stage utilizing heuristic-guided multi-task
pre-training (HMP) where heuristic-derived reference policies (e.g., through
Dijkstra's algorithm) regularize pursuer policies, and (iii) a fine-tuning
stage that employs PSRO to generate pursuer policies on designated PEGs.
Finally, we perform extensive experiments on synthetic and real-world maps,
showcasing Grasper's significant superiority over baselines in terms of
solution quality and generalizability. We demonstrate that Grasper provides a
versatile approach for solving pursuit-evasion problems across a broad range of
scenarios, enabling practical deployment in real-world situations.

Grasper 是一种基于图神经网络和超网络的通用追逐 - 逃避问题解决方法，通过高质量的解决方案在各种情景下提供优越性能，实现在真实世界中的实际部署。