  This paper explores a rapid, optimal smooth path-planning algorithm for
robots (e.g., autonomous vehicles) in point cloud environments. Derivative maps
such as dense point clouds, mesh maps, Octomaps, etc. are frequently used for
path planning purposes. A bi-directional target-oriented point planning
algorithm, directly using point clouds to compute the optimized and dynamically
feasible trajectories, is presented in this paper. This approach searches for
obstacle-free, low computational cost, smooth, and dynamically feasible paths
by analyzing a point cloud of the target environment, using a modified
bi-directional and RRT-connect-based path planning algorithm, with a k-d
tree-based obstacle avoidance strategy and three-step optimization. This
presented approach bypasses the common 3D map discretization, directly
leveraging point cloud data and it can be separated into two parts: modified
RRT-based algorithm core and the three-step optimization. Simulations on 8 2D
maps with different configurations and characteristics are presented to show
the efficiency and 2D performance of the proposed algorithm. Benchmark
comparison and evaluation with other RRT-based algorithms like RRT, B-RRT, and
RRT star are also shown in the paper. Finally, the proposed algorithm
successfully achieved different levels of mission goals on three 3D point cloud
maps with different densities. The whole simulation proves that not only can
our algorithm achieves a better performance on 2D maps compared with other
algorithms, but also it can handle different tasks(ground vehicles and UAV
applications) on different 3D point cloud maps, which shows the high
performance and robustness of the proposed algorithm. The algorithm is
open-sourced at \url{https://github.com/zhz03/BTO-RRT}
