Relatively Lazy: Indoor-Outdoor Navigation Using Vision and GNSS
This work addresses the challenge of reliable navigation for robots in environments where visual localization alone is insufficient, though it appears incremental as it builds on existing Visual Teach and Repeat methods.
The paper tackles the problem of autonomous navigation in mixed indoor-outdoor environments by combining visual and GNSS sensors to enable robust path following without requiring absolute state estimation or map optimization. The result is validated on a real robot with 3.5km of autonomous route repeating, achieving smooth error signals despite sensor dropouts.
Visual Teach and Repeat has shown relative navigation is a robust and efficient solution for autonomous vision-based path following in difficult environments. Adding additional absolute sensors such as Global Navigation Satellite Systems (GNSS) has the potential to expand the domain of Visual Teach and Repeat to environments where the ability to visually localize is not guaranteed. Our method of lazy mapping and delaying estimation until a path-tracking error is needed avoids the need to estimate absolute states. As a result, map optimization is not required and paths can be driven immediately after being taught. We validate our approach on a real robot through an experiment in a joint indoor-outdoor environment comprising 3.5km of autonomous route repeating across a variety of lighting conditions. We achieve smooth error signals throughout the runs despite large sections of dropout for each sensor.