3,020 research outputs found
A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
We propose a set of compositional design patterns to describe a large variety
of systems that combine statistical techniques from machine learning with
symbolic techniques from knowledge representation. As in other areas of
computer science (knowledge engineering, software engineering, ontology
engineering, process mining and others), such design patterns help to
systematize the literature, clarify which combinations of techniques serve
which purposes, and encourage re-use of software components. We have validated
our set of compositional design patterns against a large body of recent
literature.Comment: 12 pages,55 reference
Keyframe-based visual–inertial odometry using nonlinear optimization
Combining visual and inertial measurements has become popular in mobile robotics, since the two sensing modalities offer complementary characteristics that make them the ideal choice for accurate visual–inertial odometry or simultaneous localization and mapping (SLAM). While historically the problem has been addressed with filtering, advancements in visual estimation suggest that nonlinear optimization offers superior accuracy, while still tractable in complexity thanks to the sparsity of the underlying problem. Taking inspiration from these findings, we formulate a rigorously probabilistic cost function that combines reprojection errors of landmarks and inertial terms. The problem is kept tractable and thus ensuring real-time operation by limiting the optimization to a bounded window of keyframes through marginalization. Keyframes may be spaced in time by arbitrary intervals, while still related by linearized inertial terms. We present evaluation results on complementary datasets recorded with our custom-built stereo visual–inertial hardware that accurately synchronizes accelerometer and gyroscope measurements with imagery. A comparison of both a stereo and monocular version of our algorithm with and without online extrinsics estimation is shown with respect to ground truth. Furthermore, we compare the performance to an implementation of a state-of-the-art stochastic cloning sliding-window filter. This competitive reference implementation performs tightly coupled filtering-based visual–inertial odometry. While our approach declaredly demands more computation, we show its superior performance in terms of accuracy
Department of Computer Science Activity 1998-2004
This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period
PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning
We present PRM-RL, a hierarchical method for long-range navigation task
completion that combines sampling based path planning with reinforcement
learning (RL). The RL agents learn short-range, point-to-point navigation
policies that capture robot dynamics and task constraints without knowledge of
the large-scale topology. Next, the sampling-based planners provide roadmaps
which connect robot configurations that can be successfully navigated by the RL
agent. The same RL agents are used to control the robot under the direction of
the planning, enabling long-range navigation. We use the Probabilistic Roadmaps
(PRMs) for the sampling-based planner. The RL agents are constructed using
feature-based and deep neural net policies in continuous state and action
spaces. We evaluate PRM-RL, both in simulation and on-robot, on two navigation
tasks with non-trivial robot dynamics: end-to-end differential drive indoor
navigation in office environments, and aerial cargo delivery in urban
environments with load displacement constraints. Our results show improvement
in task completion over both RL agents on their own and traditional
sampling-based planners. In the indoor navigation task, PRM-RL successfully
completes up to 215 m long trajectories under noisy sensor conditions, and the
aerial cargo delivery completes flights over 1000 m without violating the task
constraints in an environment 63 million times larger than used in training.Comment: 9 pages, 7 figure
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