5 research outputs found
Near-Optimal Budgeted Data Exchange for Distributed Loop Closure Detection
Inter-robot loop closure detection is a core problem in collaborative SLAM
(CSLAM). Establishing inter-robot loop closures is a resource-demanding
process, during which robots must consume a substantial amount of
mission-critical resources (e.g., battery and bandwidth) to exchange sensory
data. However, even with the most resource-efficient techniques, the resources
available onboard may be insufficient for verifying every potential loop
closure. This work addresses this critical challenge by proposing a
resource-adaptive framework for distributed loop closure detection. We seek to
maximize task-oriented objectives subject to a budget constraint on total data
transmission. This problem is in general NP-hard. We approach this problem from
different perspectives and leverage existing results on monotone submodular
maximization to provide efficient approximation algorithms with performance
guarantees. The proposed approach is extensively evaluated using the KITTI
odometry benchmark dataset and synthetic Manhattan-like datasets.Comment: RSS 2018 Extended Versio
Hydra-Multi: Collaborative Online Construction of 3D Scene Graphs with Multi-Robot Teams
3D scene graphs have recently emerged as an expressive high-level map
representation that describes a 3D environment as a layered graph where nodes
represent spatial concepts at multiple levels of abstraction (e.g., objects,
rooms, buildings) and edges represent relations between concepts (e.g.,
inclusion, adjacency). This paper describes Hydra-Multi, the first multi-robot
spatial perception system capable of constructing a multi-robot 3D scene graph
online from sensor data collected by robots in a team. In particular, we
develop a centralized system capable of constructing a joint 3D scene graph by
taking incremental inputs from multiple robots, effectively finding the
relative transforms between the robots' frames, and incorporating loop closure
detections to correctly reconcile the scene graph nodes from different robots.
We evaluate Hydra-Multi on simulated and real scenarios and show it is able to
reconstruct accurate 3D scene graphs online. We also demonstrate Hydra-Multi's
capability of supporting heterogeneous teams by fusing different map
representations built by robots with different sensor suites.Comment: 8 pages, 10 figure
Compute-Bound and Low-Bandwidth Distributed 3D Graph-SLAM
This article describes a new approach for distributed 3D SLAM map building.
The key contribution of this article is the creation of a distributed
graph-SLAM map-building architecture responsive to bandwidth and computational
needs of the robotic platform. Responsiveness is afforded by the integration of
a 3D point cloud to plane cloud compression algorithm that approximates dense
3D point cloud using local planar patches. Compute bound platforms may restrict
the computational duration of the compression algorithm and low-bandwidth
platforms can restrict the size of the compression result. The backbone of the
approach is an ultra-fast adaptive 3D compression algorithm that transforms
swaths of 3D planar surface data into planar patches attributed with image
textures. Our approach uses DVO SLAM, a leading algorithm for 3D mapping, and
extends it by computationally isolating map integration tasks from local
Guidance, Navigation, and Control tasks and includes an addition of a network
protocol to share the compressed plane clouds. The joint effect of these
contributions allows agents with 3D sensing capabilities to calculate and
communicate compressed map information commensurate with their onboard
computational resources and communication channel capacities. This opens SLAM
mapping to new categories of robotic platforms that may have computational and
memory limits that prohibit other SLAM solutions