2 research outputs found
Robot-centric elevation mapping with uncertainty estimates
This paper addresses the local terrain mapping process for an autonomous robot. Building upon an onboard range measurement sensor and an existing robot pose estimation, we formulate a novel elevation mapping method from a robot-centric perspective. This formulation can explicitly handle drift of the robot pose estimation which occurs for many autonomous robots. Our mapping approach fully incorporates the distance sensor measurement uncertainties and the six-dimensional pose covariance of the robot. We introduce a computationally efficient formulation of the map fusion process, which allows for mapping a terrain at high update rates. Finally, our approach is demonstrated on a quadrupedal robot walking over obstacles
Learning from Experience for Rapid Generation of Local Car Maneuvers
Being able to rapidly respond to the changing scenes and traffic situations
by generating feasible local paths is of pivotal importance for car autonomy.
We propose to train a deep neural network (DNN) to plan feasible and
nearly-optimal paths for kinematically constrained vehicles in small constant
time. Our DNN model is trained using a novel weakly supervised approach and a
gradient-based policy search. On real and simulated scenes and a large set of
local planning problems, we demonstrate that our approach outperforms the
existing planners with respect to the number of successfully completed tasks.
While the path generation time is about 40 ms, the generated paths are smooth
and comparable to those obtained from conventional path planners