论文标题
使用语义感知反馈在部分熟悉的平面环境中的反应性导航
Reactive Navigation in Partially Familiar Planar Environments Using Semantic Perceptual Feedback
论文作者
论文摘要
本文通过求助于在线反应性方案来解决平面导航问题，该方案利用了SLAM和视觉对象识别的最新进展，以根据熟悉对象的离线目录来重铸先前的几何知识。由此产生的矢量场策划者保证了与任意指定的目标的融合，避免了固定但任意地将实例放置在目录中的实例以及完全未知的固定障碍的碰撞，只要它们强烈凸出并且良好分离。我们通过补充了在不同设置中的轮式和腿部平台上的物理实现来说明这种确定性反应性计划者的通用鲁棒性属性以及该算法的相对适度的计算成本。
This paper solves the planar navigation problem by recourse to an online reactive scheme that exploits recent advances in SLAM and visual object recognition to recast prior geometric knowledge in terms of an offline catalogue of familiar objects. The resulting vector field planner guarantees convergence to an arbitrarily specified goal, avoiding collisions along the way with fixed but arbitrarily placed instances from the catalogue as well as completely unknown fixed obstacles so long as they are strongly convex and well separated. We illustrate the generic robustness properties of such deterministic reactive planners as well as the relatively modest computational cost of this algorithm by supplementing an extensive numerical study with physical implementation on both a wheeled and legged platform in different settings.
