Robust and Efficient Multirobot 3-D Mapping Merging With Octree-Based Occupancy Grids

Abstract

Recent robotics applications require 3-D representations of the environments. In many cases, it is not feasible for a single robot to map the entire environment. In these cases, it is necessary for a team of robots to build maps independently and merge them into a single global map. In this paper, octree-based occupancy grids, which are currently the state-of-the-art 3-D map representation, are applied to the problem of multirobot mapping. Octrees allow large environments to be mapped efficiently, in terms of memory usage, while still providing sufficiently fine resolution where required. The main contribution of this work lies in the definition and validation of a system, which use map data from commonly mapped portions of the environment with registration techniques, such that maps are merged coherently despite measurement noise and error in the relative transformations between maps for experimental data sets. The system defined can then be used in a complete solution that is ported to mobile robots. The results demonstrate that octree occupancy grids are a suitable representation for multirobot 3-D mapping, but that the proposed techniques for improving erroneous transformation estimates between map frames allow multiple maps to be merged efficiently and robustly.

AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming

“…The field of multi-robot cooperative mapping is a recurrent and relevant problem in literature and, as previously introduced, several solutions have been presented by means of either multi-robot SLAM algorithms or map merging/map registration strategies, in both 2D ( [5], [10], [11]) and 3D ( [6], [12], [13]) settings. Registration of point cloud based maps can also be considered as an instance of the more general point set registration problem [14], [15].…”

Efficient Decentralized Collaborative Mapping for Outdoor Environments

“…1) Preparation for Registration step: The computed intersecting volume of the two maps A sel and B sel is denoted A int and B int and can be obtained from the exchanged map bounds [17]. In order to improve the computation speed, point clouds A int to B int first go through a down-sampling process in order to reduce the number of points to align of our clouds.…”

“…Furthermore, an accurate transformation between maps was supposed being known, however in real applications, the transform matrix between two robots map is usually only coarsely estimation from sensor observations. This work was extended in [17], where the subset of points included in the common region is extracted prior to the merge. Then, the merging process refines the transformation estimate between maps by ICP registration [4] In this paper, we assume two types of environment representation during the process: 3D point clouds format for the local stage and grid representation for the global one.…”

A framework of probabilistic 3D map fusion for collaborative robots

“…process. The fusion of 3D volumetric maps generated using 3D probabilistic map is first proposed in [70], which utilizes ICP [72]…”

“…Then, the key issue is to integrate the probabilistic in-formation in partial maps into a global consistent map. In many of the previous methods, once the transformations between map coordinate frames are estimated, the final maps are generated by simply stitching the overlapped data [22] or averaging the occupancy probability value of corresponding pairs on voxel-wise level [70],…”