989 research outputs found
Search-based Motion Planning for Aggressive Flight in SE(3)
Quadrotors with large thrust-to-weight ratios are able to track aggressive
trajectories with sharp turns and high accelerations. In this work, we develop
a search-based trajectory planning approach that exploits the quadrotor
maneuverability to generate sequences of motion primitives in cluttered
environments. We model the quadrotor body as an ellipsoid and compute its
flight attitude along trajectories in order to check for collisions against
obstacles. The ellipsoid model allows the quadrotor to pass through gaps that
are smaller than its diameter with non-zero pitch or roll angles. Without any
prior information about the location of gaps and associated attitude
constraints, our algorithm is able to find a safe and optimal trajectory that
guides the robot to its goal as fast as possible. To accelerate planning, we
first perform a lower dimensional search and use it as a heuristic to guide the
generation of a final dynamically feasible trajectory. We analyze critical
discretization parameters of motion primitive planning and demonstrate the
feasibility of the generated trajectories in various simulations and real-world
experiments.Comment: 8 pages, submitted to RAL and ICRA 201
Attention and Anticipation in Fast Visual-Inertial Navigation
We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to
estimate its state using an on-board camera and an inertial sensor, without any
prior knowledge of the external environment. We consider the case in which the
robot can allocate limited resources to VIN, due to tight computational
constraints. Therefore, we answer the following question: under limited
resources, what are the most relevant visual cues to maximize the performance
of visual-inertial navigation? Our approach has four key ingredients. First, it
is task-driven, in that the selection of the visual cues is guided by a metric
quantifying the VIN performance. Second, it exploits the notion of
anticipation, since it uses a simplified model for forward-simulation of robot
dynamics, predicting the utility of a set of visual cues over a future time
horizon. Third, it is efficient and easy to implement, since it leads to a
greedy algorithm for the selection of the most relevant visual cues. Fourth, it
provides formal performance guarantees: we leverage submodularity to prove that
the greedy selection cannot be far from the optimal (combinatorial) selection.
Simulations and real experiments on agile drones show that our approach ensures
state-of-the-art VIN performance while maintaining a lean processing time. In
the easy scenarios, our approach outperforms appearance-based feature selection
in terms of localization errors. In the most challenging scenarios, it enables
accurate visual-inertial navigation while appearance-based feature selection
fails to track robot's motion during aggressive maneuvers.Comment: 20 pages, 7 figures, 2 table
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