  We present SGoLAM, short for simultaneous goal localization and mapping,
which is a simple and efficient algorithm for Multi-Object Goal navigation.
Given an agent equipped with an RGB-D camera and a GPS/Compass sensor, our
objective is to have the agent navigate to a sequence of target objects in
realistic 3D environments. Our pipeline fully leverages the strength of
classical approaches for visual navigation, by decomposing the problem into two
key components: mapping and goal localization. The mapping module converts the
depth observations into an occupancy map, and the goal localization module
marks the locations of goal objects. The agent's policy is determined using the
information provided by the two modules: if a current goal is found, plan
towards the goal and otherwise, perform exploration. As our approach does not
require any training of neural networks, it could be used in an off-the-shelf
manner, and amenable for fast generalization in new, unseen environments.
Nonetheless, our approach performs on par with the state-of-the-art
learning-based approaches. SGoLAM is ranked 2nd in the CVPR 2021 MultiON
(Multi-Object Goal Navigation) challenge. We have made our code publicly
available at \emph{https://github.com/eunsunlee/SGoLAM}.
