4 accepted papers
Developing software for large scale off-road robot applications is challenging and tedious due to cost, logistics, and rigor of field testing. High-fidelity sensor-realistic simulation can speed up the development process for perception and state estimation algorithms. We focus on Lidar simulation f…
There is growing interest in applying machine learning to motion planning. Potential applications are predicting an initial seed for trajectory optimization, predicting an effective heuristic for search based planning, and even predicting a planning algorithm for adaptive motion planning systems. In…
Sensor models, which specify the distribution of sensor observations, are a widely used and integral part of robotics algorithms. Observation distributions are commonly approximated by parametric models, which are limited in their expressiveness, and may require careful design to suit an application…
We study the problem of building a sensor model for the purpose of simulation. Our work is motivated by the potential impact of realistic simulators on the development cycle of software for real robots. The case is made for building models from approximate state information, relieving the burden of…