Bayes Networks for Sonar Sensor Fusion
This work addresses sensor fusion challenges for mobile robots in uncertain environments, but it appears incremental as it builds on earlier methods by relaxing an independence assumption.
The paper tackles the problem of wide-angle sonar mapping for mobile robots by addressing uncertainties like dropouts, obstacle location uncertainty, and distance errors, with a focus on overcoming the overoptimistic independence assumption in prior work using Bayes networks to model dependencies, and simulation results support its feasibility.
Wide-angle sonar mapping of the environment by mobile robot is nontrivial due to several sources of uncertainty: dropouts due to "specular" reflections, obstacle location uncertainty due to the wide beam, and distance measurement error. Earlier papers address the latter problems, but dropouts remain a problem in many environments. We present an approach that lifts the overoptimistic independence assumption used in earlier work, and use Bayes nets to represent the dependencies between objects of the model. Objects of the model consist of readings, and of regions in which "quasi location invariance" of the (possible) obstacles exists, with respect to the readings. Simulation supports the method's feasibility. The model is readily extensible to allow for prior distributions, as well as other types of sensing operations.