  When performing manipulation-based activities such as picking objects, a
mobile robot needs to position its base at a location that supports successful
execution. To address this problem, prominent approaches typically rely on
costly grasp planners to provide grasp poses for a target object, which are
then are then analysed to identify the best robot placements for achieving each
grasp pose. In this paper, we propose instead to first find robot placements
that would not result in collision with the environment and from where picking
up the object is feasible, then evaluate them to find the best placement
candidate. Our approach takes into account the robot's reachability, as well as
RGB-D images and occupancy grid maps of the environment for identifying
suitable robot poses. The proposed algorithm is embedded in a service robotic
workflow, in which a person points to select the target object for grasping. We
evaluate our approach with a series of grasping experiments, against an
existing baseline implementation that sends the robot to a fixed navigation
goal. The experimental results show how the approach allows the robot to grasp
the target object from locations that are very challenging to the baseline
implementation.
