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Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning
The design of these low-level heuristics has a major impact on the quality of the result.
A Neural-Evolutionary Algorithm for Autonomous Transit Network Design
Planning a public transit network is a challenging optimization problem, but essential in order to realize the benefits of autonomous buses.
Neural Bee Colony Optimization: A Case Study in Public Transit Network Design
In this work we explore the combination of metaheuristics and learned neural network solvers for combinatorial optimization.
Scale-Robust Localization Using General Object Landmarks
Visual localization under large changes in scale is an important capability in many robotic mapping applications, such as localizing at low altitudes in maps built at high altitudes, or performing loop closure over long distances.
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