From local visual homing towards navigation of autonomous cleaning robots

摘要

This thesis deals with the implementation of navigation strategies for a domestic floor-cleaning robot operating on omnidirectional images as primary sensory information. Such navigation strategies enable the robot to efficiently\ncover its entire workspace while avoiding both uncleaned areas and repeated coverage. This is accomplished (i) by systematically guiding the robot along meandering lanes, i.e. along straight lanes placed next to each other at a\npredefined and constant distance and (ii) by building a map of the robot's environment to distinguish cleaned and uncleaned areas. Since domestic cleaning robots are considered consumer goods, they can only be equipped with a limited number of cheap sensors and restricted computational power. This fact poses additional challenges onto the design of navigation strategies for domestic\nfloor-cleaning robots. \nThe navigation strategies described in this thesis use omnidirectional images, in our case panoramic images with a full 360° horizontal field of view. We consider omnidirectional cameras an appropriate choice because they (i) are relatively cheap sensors, (ii) provide dense sensory information about the robot's environment, and (iii) are multi-purpose sensors applicable to further\naspects of cleaningrobot navigation beyond the scope of this thesis (e.g. obstacle detection, visual odometry, or user interaction). We characterize a position in space by the entire omnidirectional image acquired at this place\n(hence the methods belong to the class of appearance-based navigation methods) without detecting visible features in the image. Several places are integrated into a dense topo-metric map of the robot's environment. Such maps (i) offer a metrical position estimate required for guiding the robot along meandering lanes, (ii) have a spatial resolution which is fine enough for accurate\nnavigation, (iii) can be easily built from the available sensor data, and (iv) allow for efficiently operating on the maps. Spatial relations between places stored in the map are estimated by applying a local visual homing method. Such methods are parsimonious yet robust and accurate methods for partial ego-motion estimation from visual information. They recover the direction (but not the\ndistance) of the translation and the rotation of the robot's motion between two images acquired in direct vicinity of each other without physically moving\nbetween places. As far as we know, our navigation methods are the first application of omnidirectional vision, dense topo-metric maps, and local visual homing for the control of cleaning robots. Hence, this thesis is also a\nfeasibility study to prove the applicability of these concepts for navigation of cleaning robots. Since a complete control scheme for a cleaning robot is beyond\nthe scope of this thesis, we propose two essential substrategies of such a control scheme: (i) vision-based trajectory control and mapping and (ii) visual\ndetection of already cleaned areas.\nRegarding trajectory control and mapping, we propose a mostly vision-based controller for covering a rectangular area of the entire workspace by meandering\nlanes. While moving along a lane (and cleaning), the robot adds snapshots at regular distances to its dense topo-metric map, which are used on the subsequent\nlane to estimate the robot's current distance to the previous lane. For this purpose, the bearing from the current position towards at least two snapshots\nstored along the previous lane is taken by applying local visual homing. The bearing information and an odometry-based estimate of the distance between the\ntwo considered snapshots are fused in order to estimate the robot's current distance to the previous lane. The robot is kept on a lane parallel to the previous one by keeping the distance to the previous lane at a predefined value. Instead of estimating the robot's full pose as performed by common navigation strategies, we only estimate the distance to the previous lan