Discrete and Continuous, Probabilistic Anticipation for Autonomous Robots in Urban Environments
This work addresses the challenge of safe navigation for autonomous robots by improving anticipation of obstacle behaviors, though it appears incremental as it builds on existing probabilistic methods with optimizations for real-time implementation.
The paper tackles the problem of predicting future states of dynamic objects for autonomous robots in urban environments by developing a probabilistic anticipation algorithm using predictive Gaussian mixture models, and demonstrates through simulations and evaluations that it can accurately anticipate probability distributions over future states of nonlinear systems.
This paper develops a probabilistic anticipation algorithm for dynamic objects observed by an autonomous robot in an urban environment. Predictive Gaussian mixture models are used due to their ability to probabilistically capture continuous and discrete obstacle decisions and behaviors; the predictive system uses the probabilistic output (state estimate and covariance) of a tracking system, and map of the environment to compute the probability distribution over future obstacle states for a specified anticipation horizon. A Gaussian splitting method is proposed based on the sigma-point transform and the nonlinear dynamics function, which enables increased accuracy as the number of mixands grows. An approach to caching elements of this optimal splitting method is proposed, in order to enable real-time implementation. Simulation results and evaluations on data from the research community demonstrate that the proposed algorithm can accurately anticipate the probability distributions over future states of nonlinear systems.