  This article presents a novel and flexible multitask multilayer Bayesian
mapping framework with readily extendable attribute layers. The proposed
framework goes beyond modern metric-semantic maps to provide even richer
environmental information for robots in a single mapping formalism while
exploiting intralayer and interlayer correlations. It removes the need for a
robot to access and process information from many separate maps when performing
a complex task, advancing the way robots interact with their environments. To
this end, we design a multitask deep neural network with attention mechanisms
as our front-end to provide heterogeneous observations for multiple map layers
simultaneously. Our back-end runs a scalable closed-form Bayesian inference
with only logarithmic time complexity. We apply the framework to build a dense
robotic map including metric-semantic occupancy and traversability layers.
Traversability ground truth labels are automatically generated from
exteroceptive sensory data in a self-supervised manner. We present extensive
experimental results on publicly available datasets and data collected by a 3D
bipedal robot platform and show reliable mapping performance in different
environments. Finally, we also discuss how the current framework can be
extended to incorporate more information such as friction, signal strength,
temperature, and physical quantity concentration using Gaussian map layers. The
software for reproducing the presented results or running on customized data is
made publicly available.
