  In recent years, the demand for service robots capable of executing tasks
beyond autonomous navigation has grown. In the future, service robots will be
expected to perform complex tasks like 'Set table for dinner'. High-level tasks
like these, require, among other capabilities, the ability to retrieve multiple
targets. This paper delves into the challenge of locating multiple targets in
an environment, termed 'Find my Objects.' We present a novel heuristic designed
to facilitate robots in conducting a preferential search for multiple targets
in indoor spaces. Our approach involves a Semantic SLAM framework that combines
semantic object recognition with geometric data to generate a multi-layered
map. We fuse the semantic maps with probabilistic priors for efficient
inferencing. Recognizing the challenges introduced by obstacles that might
obscure a navigation goal and render standard point-to-point navigation
strategies less viable, our methodology offers resilience to such factors.
Importantly, our method is adaptable to various object detectors, RGB-D SLAM
techniques, and local navigation planners. We demonstrate the 'Find my Objects'
task in real-world indoor environments, yielding quantitative results that
attest to the effectiveness of our methodology. This strategy can be applied in
scenarios where service robots need to locate, grasp, and transport objects,
taking into account user preferences. For a brief summary, please refer to our
video: https://tinyurl.com/PrefTargetSearch
