Distributed Optimization with Consensus Constraint for Multi-Robot Semantic Octree Mapping

Abstract-- This work develops a distributed optimization algorithm for multi-robot 3-D semantic mapping using streaming range and visual observations and single-hop communication. Our approach relies on gradient-based optimization of the observation log-likelihood of each robot subject to a map consensus constraint to build a common multi-class map of the environment. This formulation leads to closed-form updates which resemble Bayes rule with one-hop prior averaging. To reduce the amount of information exchanged among the robots, we utilize an octree data structure that compresses the multiclass map distribution using adaptive-resolution. Figure 1: Semantically annotated point cloud obtained from a range and category observation z Dense semantically-rich environment representations, such as multi-class volumetric maps [1], can be built online by distribution by each robot. Through a simulated experiment, mobile robots thanks to the availability of on-board GPUaccelerated we show that our algorithm leads to globally consistent segmentation models.