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By utilizing only depth information, the paper introduces a novel butefficient local planning approach that enhances not only computationalefficiency but also planning performances for memoryless local planners. Thesampling is first proposed to be based on the depth data which can identify andeliminate a specific type of in-collision trajectories in the sampled motionprimitive library. More specifically, all the obscured primitives' endpointsare found through querying the depth values and excluded from the sampled set,which can significantly reduce the computational workload required in collisionchecking. On the other hand, we furthermore propose a steering mechanism alsobased on the depth information to effectively prevent an autonomous vehiclefrom getting stuck when facing a large convex obstacle, providing a higherlevel of autonomy for a planning system. Our steering technique istheoretically proved to be complete in scenarios of convex obstacles. Toevaluate effectiveness of the proposed DEpth based both Sampling and Steering(DESS) methods, we implemented them in the synthetic environments where aquadrotor was simulated flying through a cluttered region with multiplesize-different obstacles. The obtained results demonstrate that the proposedapproach can considerably decrease computing time in local planners, where moretrajectories can be evaluated while the best path with much lower cost can befound. More importantly, the success rates calculated by the fact that therobot successfully navigated to the destinations in different testing scenariosare always higher than 99.6% on average.

Depth-based Sampling and Steering Constraints for Memoryless Local Planners