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Localization under consistent assumptions over dynamics
Accurate maps are a prerequisite for virtually all autonomous vehicle tasks.
Most state-of-the-art maps assume a static world, and therefore dynamic objects
are filtered out of the measurements. However, this division ignores movable
but non-moving, i.e. semi-static, objects, which are usually recorded in the
map and treated as static objects, violating the static world assumption,
causing error in the localization. In this paper, we present a method for
modeling moving and movable objects for matching the map and the measurements
consistently. This reduces the error resulting from inconsistent categorization
and treatment of non-static measurements. A semantic segmentation network is
used to categorize the measurements into static and semi-static classes, and a
background subtraction-based filtering method is used to remove dynamic
measurements. Experimental comparison against a state-of-the-art baseline
solution using real-world data from Oxford Radar RobotCar data set shows that
consistent assumptions over dynamics increase localization accuracy.Comment: Submitted to IEEE-ICRA-202
Physics-based Motion Planning with Temporal Logic Specifications
One of the main foci of robotics is nowadays centered in providing a great
degree of autonomy to robots. A fundamental step in this direction is to give
them the ability to plan in discrete and continuous spaces to find the required
motions to complete a complex task. In this line, some recent approaches
describe tasks with Linear Temporal Logic (LTL) and reason on discrete actions
to guide sampling-based motion planning, with the aim of finding
dynamically-feasible motions that satisfy the temporal-logic task
specifications. The present paper proposes an LTL planning approach enhanced
with the use of ontologies to describe and reason about the task, on the one
hand, and that includes physics-based motion planning to allow the purposeful
manipulation of objects, on the other hand. The proposal has been implemented
and is illustrated with didactic examples with a mobile robot in simple
scenarios where some of the goals are occupied with objects that must be
removed in order to fulfill the task.Comment: The 20th World Congress of the International Federation of Automatic
Control, 9-14 July 201
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