14,029 research outputs found
Route Planning in Transportation Networks
We survey recent advances in algorithms for route planning in transportation
networks. For road networks, we show that one can compute driving directions in
milliseconds or less even at continental scale. A variety of techniques provide
different trade-offs between preprocessing effort, space requirements, and
query time. Some algorithms can answer queries in a fraction of a microsecond,
while others can deal efficiently with real-time traffic. Journey planning on
public transportation systems, although conceptually similar, is a
significantly harder problem due to its inherent time-dependent and
multicriteria nature. Although exact algorithms are fast enough for interactive
queries on metropolitan transit systems, dealing with continent-sized instances
requires simplifications or heavy preprocessing. The multimodal route planning
problem, which seeks journeys combining schedule-based transportation (buses,
trains) with unrestricted modes (walking, driving), is even harder, relying on
approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4,
previously published by Microsoft Research. This work was mostly done while
the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at
Microsoft Research Silicon Valle
Guided Cooperation in Hierarchical Reinforcement Learning via Model-based Rollout
Goal-conditioned hierarchical reinforcement learning (HRL) presents a
promising approach for enabling effective exploration in complex long-horizon
reinforcement learning (RL) tasks via temporal abstraction. Yet, most
goal-conditioned HRL algorithms focused on the subgoal discovery, regardless of
inter-level coupling. In essence, for hierarchical systems, the increased
inter-level communication and coordination can induce more stable and robust
policy improvement. Here, we present a goal-conditioned HRL framework with
Guided Cooperation via Model-based Rollout (GCMR), which estimates forward
dynamics to promote inter-level cooperation. The GCMR alleviates the
state-transition error within off-policy correction through a model-based
rollout, further improving the sample efficiency. Meanwhile, to avoid being
disrupted by these corrected but possibly unseen or faraway goals, lower-level
Q-function gradients are constrained using a gradient penalty with a
model-inferred upper bound, leading to a more stable behavioral policy.
Besides, we propose a one-step rollout-based planning to further facilitate
inter-level cooperation, where the higher-level Q-function is used to guide the
lower-level policy by estimating the value of future states so that global task
information is transmitted downwards to avoid local pitfalls. Experimental
results demonstrate that incorporating the proposed GCMR framework with ACLG, a
disentangled variant of HIGL, yields more stable and robust policy improvement
than baselines and substantially outperforms previous state-of-the-art (SOTA)
HRL algorithms in both hard-exploration problems and robotic control
Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization
Many robotics applications require precise pose estimates despite operating
in large and changing environments. This can be addressed by visual
localization, using a pre-computed 3D model of the surroundings. The pose
estimation then amounts to finding correspondences between 2D keypoints in a
query image and 3D points in the model using local descriptors. However,
computational power is often limited on robotic platforms, making this task
challenging in large-scale environments. Binary feature descriptors
significantly speed up this 2D-3D matching, and have become popular in the
robotics community, but also strongly impair the robustness to perceptual
aliasing and changes in viewpoint, illumination and scene structure. In this
work, we propose to leverage recent advances in deep learning to perform an
efficient hierarchical localization. We first localize at the map level using
learned image-wide global descriptors, and subsequently estimate a precise pose
from 2D-3D matches computed in the candidate places only. This restricts the
local search and thus allows to efficiently exploit powerful non-binary
descriptors usually dismissed on resource-constrained devices. Our approach
results in state-of-the-art localization performance while running in real-time
on a popular mobile platform, enabling new prospects for robotics research.Comment: CoRL 2018 Camera-ready (fix typos and update citations
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
- …