21 research outputs found
Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps
Grid maps are widely used in robotics to represent obstacles in the
environment and differentiating dynamic objects from static infrastructure is
essential for many practical applications. In this work, we present a methods
that uses a deep convolutional neural network (CNN) to infer whether grid cells
are covering a moving object or not. Compared to tracking approaches, that use
e.g. a particle filter to estimate grid cell velocities and then make a
decision for individual grid cells based on this estimate, our approach uses
the entire grid map as input image for a CNN that inspects a larger area around
each cell and thus takes the structural appearance in the grid map into account
to make a decision. Compared to our reference method, our concept yields a
performance increase from 83.9% to 97.2%. A runtime optimized version of our
approach yields similar improvements with an execution time of just 10
milliseconds.Comment: This is a shorter version of the masters thesis of Florian Piewak and
it was accapted at IV 201
Learned Enrichment of Top-View Grid Maps Improves Object Detection
We propose an object detector for top-view grid maps which is additionally
trained to generate an enriched version of its input. Our goal in the joint
model is to improve generalization by regularizing towards structural knowledge
in form of a map fused from multiple adjacent range sensor measurements. This
training data can be generated in an automatic fashion, thus does not require
manual annotations. We present an evidential framework to generate training
data, investigate different model architectures and show that predicting
enriched inputs as an additional task can improve object detection performance.Comment: 6 pages, 6 figures, 4 table