3 accepted papers
This paper reports on an active simultaneous localization and mapping (SLAM) framework that leverages the Bayes tree data structure for efficient planning. Evaluating information-theoretic objective functions in the context of active SLAM is a very expensive process that requires significant computa…
This paper reports on the inclusion of a probabilistic channel model within a cooperative localization planning framework. Underwater cooperative localization reduces positioning errors by sharing sensor data across a team of underwater vehicles. Relative range constraints between vehicles are measu…
This paper reports on a Gaussian belief-space planning formulation for mobile robots that includes random measurement acquisition variables that model whether or not each measurement is actually acquired. We show that maintaining the stochasticity of these variables in the planning formulation leads…