Maplets: An Efficient Approach for Cooperative SLAM Map Building Under Communication and Computation Constraints

TL;DR

Maplets presents a novel, efficient framework for cooperative SLAM map building that minimizes communication and computation by using local maplets and a two-tier optimization process, enabling scalable exploration.

Contribution

The paper introduces a new two-tier optimization framework that efficiently combines local maplets and global transformations for cooperative SLAM under resource constraints.

Findings

Efficient two-tier optimization reduces communication overhead.

Maplets enable scalable, accurate large-scale mapping.

Compact map representations facilitate cooperative exploration.

Abstract

This article introduces an approach to facilitate cooperative exploration and mapping of large-scale, near-ground, underground, or indoor spaces via a novel integration framework for locally-dense agent map data. The effort targets limited Size, Weight, and Power (SWaP) agents with an emphasis on limiting required communications and redundant processing. The approach uses a unique organization of batch optimization engines to enable a highly efficient two-tier optimization structure. Tier I consist of agents that create and potentially share local maplets (local maps, limited in size) which are generated using Simultaneous Localization and Mapping (SLAM) map-building software and then marginalized to a more compact parameterization. Maplets are generated in an overlapping manner and used to estimate the transform and uncertainty between those overlapping maplets, providing accurate and compact odometry or delta-pose representation between maplet's local frames. The delta poses can be shared between agents, and in cases where maplets have salient features (for loop closures), the compact representation of the maplet can also be shared. The second optimization tier consists of a global optimizer that seeks to optimize those maplet-to-maplet transformations, including any loop closures identified. This can provide an accurate global "skeleton"' of the traversed space without operating on the high-density point cloud. This compact version of the map data allows for scalable, cooperative exploration with limited communication requirements where most of the individual maplets, or low fidelity renderings, are only shared if desired.