Interacting Markov Random Fields for Simultaneous Terrain Modeling and Obstacle Detection

Abstract

Autonomous navigation in outdoor environments with vegetation is difficult because available sensors make very indirect measurements on quantities of interest such as the supporting ground height and the location of obstacles. We introduce a terrain model that includes spatial constraints on these quantities to exploit structure found in outdoor domains and use available sensor data more effectively. The model consists of a latent variable that establishes a prior that favors vegetation of a similar height, plus multiple Markov random fields that incorporate neighborhood interactions and impose a prior on smooth ground and class continuity. These Markov random fields interact through a hidden semi-Markov model that enforces a prior on the vertical structure of elements in the environment. The system runs in real-time and has been trained and tested using real data from an agricultural setting. Results show that exploiting the 3D structure inherent in outdoor domains significantly improves ground height estimates and obstacle detection accuracy.