Abstract
Outdoor environments pose multiple challenges for the visual navigation of robots, like changing illumination conditions, seasonal changes, dynamic environments and non-planar terrain. Illumination changes are mostly caused by the movement of the Sun and by changing cloud cover. Moving clouds themselves also are a dynamic aspect of a visual scene. For visual homing algorithms, which compute the direction to a previously visited place by comparing the current view with a snapshot taken at that place, in particular, the changing cloud cover poses a problem, since cloud movements do not correspond to movements of the camera and thus constitute misleading information. We propose an edge-filtering method operating on linearly-transformed RGB channels, which reliably detects edges in the ground region of the image while suppressing edges in the sky region. To fulfill this criterion, the factors for the linear transformation of the RGB channels are optimized systematically concerning this special requirement. Furthermore, we test the proposed linear transformation on an existing visual homing algorithm (MinWarping) and show that the performance of the visual homing method is significantly improved compared to the use of edge-filtering methods on alternative color information.
Highlights
Navigation is a fundamental task for outdoor robots, as it allows the robot to operate autonomously in an unknown environment
We test the proposed linear transformation on an existing visual homing algorithm (MinWarping) and show that the performance of the visual homing method is significantly improved compared to the use of edge-filtering methods on alternative color information
We propose an edge-detection method based on a linear transformation of the RGB channels for detecting reliable edges in the ground region of the image while simultaneously suppressing edges in the sky region
Summary
Navigation is a fundamental task for outdoor robots, as it allows the robot to operate autonomously in an unknown environment. The applications for autonomous outdoor robots range from consumer-oriented products like lawn mowers to search-and-rescue robots [1,2]. E.g., differential GPS and LIDAR, exist to allow autonomous navigation of robots. LIDAR devices contain moving mechanical parts, have a limited life-time and pose technological challenges in production. With these limitations in mind, cameras are attractive sensors, as they are low-cost products without moving parts and can be used for several different tasks, such as navigation, terrain classification [4,5] and obstacle avoidance. Panoramic images allow for separation of rotational and translational motion components [6] and simplify visual navigation methods as the visible image content is independent of the robot’s orientation
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