| Abstrakt: |
Visual-teach-and-repeat (VT&R) systems have proven extremely useful for practical robot autonomy where the global positioning system is either unavailable or unreliable, examples include tramming for underground mining using a planar laser scanner as well as a return-to-lander function for planetary exploration using a stereo-or laser-based camera. By embedding local appearance/metric information along an arbitrarily long path, it becomes possible to re-drive the path without the need for a single privileged coordinate frame and using only modest computational resources. For a certain class of long-term autonomy problems (e.g., repeatable long-range driving), VT&R appears to offer a simple yet scalable solution. Beyond single paths, we envision that networks of reusable paths could be established and shared from one robot to another to enable practical tasks such as surveillance, delivery (e.g., mail, hospitals, factories, warehouses), worksite operations (e.g., construction, mining), and autonomous roadways. However, for lifelong operations on reusable paths, robustness to a variety of environmental changes, both transient and permanent, is required. In this paper, we relate our experiences and lessons learned with the three above-mentioned implementations of VT&R systems. Based on this, we enumerate both the benefits and challenges of reusable paths that we see moving forwards. We discuss one such challenge, lighting-invariance, in detail and present our progess in overcoming it. [ABSTRACT FROM PUBLISHER] |
