  By utilizing only depth information, the paper introduces a novel but
efficient local planning approach that enhances not only computational
efficiency but also planning performances for memoryless local planners. The
sampling is first proposed to be based on the depth data which can identify and
eliminate a specific type of in-collision trajectories in the sampled motion
primitive library. More specifically, all the obscured primitives' endpoints
are found through querying the depth values and excluded from the sampled set,
which can significantly reduce the computational workload required in collision
checking. On the other hand, we furthermore propose a steering mechanism also
based on the depth information to effectively prevent an autonomous vehicle
from getting stuck when facing a large convex obstacle, providing a higher
level of autonomy for a planning system. Our steering technique is
theoretically proved to be complete in scenarios of convex obstacles. To
evaluate effectiveness of the proposed DEpth based both Sampling and Steering
(DESS) methods, we implemented them in the synthetic environments where a
quadrotor was simulated flying through a cluttered region with multiple
size-different obstacles. The obtained results demonstrate that the proposed
approach can considerably decrease computing time in local planners, where more
trajectories can be evaluated while the best path with much lower cost can be
found. More importantly, the success rates calculated by the fact that the
robot successfully navigated to the destinations in different testing scenarios
are always higher than 99.6% on average.
