  Navigating dynamic environments requires the robot to generate collision-free
trajectories and actively avoid moving obstacles. Most previous works designed
path planning algorithms based on one single map representation, such as the
geometric, occupancy, or ESDF map. Although they have shown success in static
environments, due to the limitation of map representation, those methods cannot
reliably handle static and dynamic obstacles simultaneously. To address the
problem, this paper proposes a gradient-based B-spline trajectory optimization
algorithm utilizing the robot's onboard vision. The depth vision enables the
robot to track and represent dynamic objects geometrically based on the voxel
map. The proposed optimization first adopts the circle-based guide-point
algorithm to approximate the costs and gradients for avoiding static obstacles.
Then, with the vision-detected moving objects, our receding-horizon distance
field is simultaneously used to prevent dynamic collisions. Finally, the
iterative re-guide strategy is applied to generate the collision-free
trajectory. The simulation and physical experiments prove that our method can
run in real-time to navigate dynamic environments safely. Our software is
available on GitHub as an open-source package.
