* @brief Clear the container */

callbacks by the L-BFGS optimizer */

This will update m_mesh from which we create the ConvexHull object that getConvexHull function returns

* @brief * * @return vec_E<Matf<N_DEG + 1, N_DIM>> */

* @brief Decomposition thread * @param path The path to dilate * @param offset_x offset added to the long semi-axis, default is 0 */

* @brief Infalte the line segment * @param radius the offset added to the long semi-axis */

* @brief Decomposition thread * @param path_raw The path to dilate * @param iter_num Max iteration number * @param offset_x offset adde

// real ids used: {id, id+1000}

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Calculate the distance from point

Uniformly sample path into many segments

develop with signed distance for current simulation, they add only vehicles with the same size. in the future, more size can be added 111

* @brief Get the end point of the parameterization */

* @brief Return evaluated vector * @param s evaluation point * @param d derivative to take */

* @brief Return evaluation of spline at given parameterization * @param s parameterization, s should be in the vector domain * @param d derivate

real ids used: {1000*id ~ (arrow nums)+1000*id}

real ids used: {id, id+1}

@yuwei

@yuwei

@yuwei

@yuwei: get COG point

Computes convex hull for a given point cloud. This function assumes that the vertex data resides in memory in the following format: x_0,y_0,z_0,x_1,y_

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@yuwei

Find indices of extreme values (max x, min x, max y, min y, max z, min z) for the given point cloud

getHeu

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@yuwei

use kinodynamic a* to generate a path

@hzc

@yuwei

@yuwei

@yuwei: revise to 2d case

hzc

MCJ

MCJ

Compute scale of the vertex data.

@yuwei

@yuwei

Get the constraints of SFC as \f$Ax\leq b \f$

Get the ellipsoids

Get the line