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Grid maps are widely used in robotics to represent obstacles in theenvironment and differentiating dynamic objects from static infrastructure isessential for many practical applications. In this work, we present a methodsthat uses a deep convolutional neural network (CNN) to infer whether grid cellsare covering a moving object or not. Compared to tracking approaches, that usee.g. a particle filter to estimate grid cell velocities and then make adecision for individual grid cells based on this estimate, our approach usesthe entire grid map as input image for a CNN that inspects a larger area aroundeach cell and thus takes the structural appearance in the grid map into accountto make a decision. Compared to our reference method, our concept yields aperformance increase from 83.9% to 97.2%. A runtime optimized version of ourapproach yields similar improvements with an execution time of just 10milliseconds.

Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps