Cooperative visual mapping in a heterogeneous team of mobile robots

Abstract

Abstract-Mapping is regarded as one of the most fundamental tasks for mobile robots. In this work, we present an approach that enables multiple resource-limited mobile robots to cooperatively build an image-based map of the environment and to afterwards localize in it. To achieve this, we deploy a hierarchical team of mobile robots. A parent robot possesses state-of-the-art sensors, computation power and acts as a leader. It teleoperates small child robots within its line-of-sight. In contrast to other approaches and due to the cooperation among the robots, we can relax the requirement that every robot must be able to self-localize to take part in multi-robot mapping. Additionally, our algorithm ensures the mapping of the entire area in an efficient way, i.e., it fulfills the requirements of area coverage. To test our approach, extensive experiments have been performed both in simulation and real-world. In the latter case, a team of four heterogeneous mobile robots was deployed. Besides the successful cooperation in the robot team, localization results are presented to validate the applicability of the proposed mapping procedure.

Heterogeneous Map Merging: State of the Art

“…However, voxel grids are memory inefficient, and in practice abstractions (Multi-Level Surface maps [65], 2.5-dimensional elevation maps [66]) or octree map compression [38,39,67]) are used. Besides the map types listed above many other map types exist (for example, image-based maps [68][69][70] and manifold maps [71]), but they are used comparatively rarely for robot navigation.…”

“…Hofmeister et al in [68] demonstrates the creation of heterogeneous maps with robot team, where a 'parent' robot is equipped with LIDAR and 'child' robots use cameras. When the occupancy grid map with 'parent' robot is acquired, the other robots create image maps suitable for independent localization under the guidance of parent robot.…”

“…[ [103][104][105][106][107] Aerial and ground view matching Methods address the cross-view localization problem for aerial and ground views. [68] Grid and image maps The approach addresses cooperative heterogeneous map creation for robots with various capabilities. [83] Grid and appearance maps…”

“…Map merging itself can also be computationally expensive and if it is impossible to delegate this task to a more capable team member, a robot with low processing capabilities may be unable to benefit from an improved map. Sharing of processing capabilities has been studied by many researchers, with one approach having robots delegate tasks to more capable team members [68] or cloud [36] and the other creating computing clusters [78] to solve complex tasks. In the recent years solutions have appeared that use the efficient parallel processing capabilities of GPU for multi-robot related tasks, for example, submap matching [36,54] • Available memory.…”

Constructing common height maps with various entropy-based similarity metrics and utilizing layering method for heterogeneous robot teams

“…A robot team consisting of identical mobile platforms carrying different types of onboard sensors can also be considered as a HeRT mission. Similarly, there is a collaborative solution in Hofmeister et al (2011), where the parent robot operates the child robots and gathering information from these robots with finding the relative positions over the map. Heterogeneity can also be defined as the ability to move on the ground, air and sea.…”