Visual Teach and Repeat (VT&R) 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 VT&R 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.
翻译:视觉教学和重复(VT&R)显示,相对导航是紧凑而高效的解决方案,有助于在困难的环境中沿循自主的视觉道路。加上诸如全球导航卫星系统(GNSS)等额外的绝对传感器,有可能将VT&R的域扩大到无法保证视觉定位的环境。我们的懒惰绘图和推迟估计方法,直到需要路径跟踪错误时,就不需要估计绝对状态。因此,不需要优化地图,在教授之后可以立即驱动路径。我们通过在室内外联合试验,在各种照明条件下重复3.5公里的自主线路,验证我们对于真正的机器人的做法。我们在整个运行过程中都实现平稳的错误信号,尽管每个传感器都有大量的辍学现象。