Semantic Trajectory Planning for Long-Distant Unmanned Aerial Vehicle Navigation in Urban Environments

Abstract

There has been a considerable amount of recent work on high-speed micro-aerial vehicle flight in unknown and unstructured environments. Generally these approaches either use active sensing or fly slowly enough to ensure a safe braking distance with the relatively short sensing range of passive sensors. The former generally requires carrying large and heavy LIDARs and the latter only allows flight far away from the dynamic limits of the vehicle. One of the significant challenges for high-speed flight is the computational demand of trajectory planning at sufficiently high rates and length scales required in outdoor environments. We tackle both problems in this work by leveraging semantic information derived from an RGB camera on-board the vehicle. We first describe how to use semantic information to increase the effective range of perception on certain environment classes. Second, we present a sparse representation of the environment that is sufficiently lightweight for long distance path planning. We show how our approach outperforms more traditional metric planners which seek the shortest path, demonstrate the semantic planner's capabilities in a set of simulated and excessive real-world autonomous quadrotor flights in an urban environment.

Hypergame-based Adaptive Behavior Path Planning for Combined Exploration and Visual Search

“…With respect to the key innovation in semantics-aware planning, this work relates to two sets of contributions. First, it partially relates to works that on one hand only focus on planning for safe navigation among predefined waypoints but also simultaneously consider the semantics in the environment [35][36][37]. Second, and most importantly, the proposed method relates to the narrow niche of contributions that aim to co-optimize exploration and mapping of objects/semantics of interest [38][39][40][41].…”