Data for: Motion Planning under Uncertainty in Graduated Fidelity Lattices

These are the specifications of each environment used of the manuscript "Motion Planning under Uncertainty in Graduated Fidelity Lattices".

**** ENVIRONMENTS ****
For each environment the following specifications are given. Units are meters and radians:
* Map dimensions: <DIM_X> x <DIM_Y>
* Location of the starting pose: (<x>, <y>, <heading>). <heading> = 0 is the direction of the positive X.
* Goal position: (<x>, <y>)
* min/max position of the location denied areas: (<x>, <y>)

** FIG 8
- Dimensions: 40.0 x 24.8 m
- Starting pose: (5.0, 8.0, 0.0)
- Goal position: (34.0, 19.0)
- Location denied areas:
(6.0, 0.0) to (11.0, 7.0)
(9.5, 7.0) to (11.0, 8.5)
(12.0, 11.0) to (28.0, 17.5)
(13.0, 9.0) to (15.0, 11.0)
(32.0, 15.0) to (35.0, 20.0)

** FIG 9
- Dimensions: 65.0 x 40.3 m
- Starting pose: (4.0, 16.0, 0.0)
- Goal position: (56.0, 8.0)
- Location denied areas (9a):
None
- Location denied areas (9b):
(25.0, 0.0) to (52.0, 15.5)
- Location denied areas (9c):
(25.0, 0.0) to (52.0, 15.5)
(25.0, 26.0) to (47.0, 40.0)

** FIG 10
- Dimensions: 35.0 x 25.3 m
- Starting pose: (19.0, 20.0, 3.1416)
- Goal position: (31.0, 5.0)
- Location denied areas:
(10.0, 0.0) to (35.0, 25.3)

** FIG 11
- Dimensions: 30.0 x 30.0 m
- Starting pose: (7.0, 15.0, -1.5708)
- Goal position: (5.0, 23.0)
- Location denied areas:
None

**** PPM FILE FORMAT ****
Images are provided in the PPM file format (ASCII). These files have the following structure:
# line comment
...
<N_PIXELS_X> <N_PIXELS_Y>
<RGB_VALUE_1>
<RGB_VALUE_2>
<RGB_VALUE_3>
...
<RGB_VALUE_1>
<RGB_VALUE_2>
<RGB_VALUE_3>

This is: after the comment lines (starting with "#"), the first two numbers are the image size (in pixels)
Then, three values for each RGB pixel are given. Pixels follow a row-major order.

Pixels with R < 10; G < 10; B < 10 are considered occupied. Elsewhere is free space.

Please, note that <N_PIXELS_X> <N_PIXELS_Y> contained in these files do not correspond to the dimensions of the
environments in the manuscript. Each pixel has a coordinate (X, Y) which is calculated from the resolution of the image
and the environment dimensions (DIM_X, DIM_Y):
X = PIXEL_X * (DIM_X / N_PIXELS_X)
Y = PIXEL_Y * (DIM_Y / N_PIXELS_Y)

**** OT FILE FORMAT ****
Files to use with the Octomap[1] library are also given. These were generated with Octomap v.1.7.2. These files already have the dimensions used in the experiments and require no additional processing.

[1] A. Hornung,. K.M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard, "OctoMap: An Efficient Probabilistic 3D Mapping Framework Based on Octrees" in Autonomous Robots, 2013; DOI: 10.1007/s10514-012-9321-0. Software available at http://octomap.github.com.

以下为论文《在逐次保真度晶格中的不确定性运动规划》中使用的各环境规格说明。

**** 环境 ****
对于每个环境，提供以下规格。单位为米和弧度：
* 地图尺寸：<DIM_X> x <DIM_Y>
* 起始姿态位置：(<x>, <y>, <heading>)。其中，<heading> = 0 代表正X方向。
* 目标位置：(<x>, <y>)
* 禁止区域位置的上下限：(<x>, <y>)

** 图 8
- 尺寸：40.0 x 24.8 米
- 起始姿态： (5.0, 8.0, 0.0)
- 目标位置： (34.0, 19.0)
- 禁止区域位置：
(6.0, 0.0) 至 (11.0, 7.0)
(9.5, 7.0) 至 (11.0, 8.5)
(12.0, 11.0) 至 (28.0, 17.5)
(13.0, 9.0) 至 (15.0, 11.0)
(32.0, 15.0) 至 (35.0, 20.0)

** 图 9
- 尺寸：65.0 x 40.3 米
- 起始姿态： (4.0, 16.0, 0.0)
- 目标位置： (56.0, 8.0)
- 禁止区域位置（9a）：
无
- 禁止区域位置（9b）：
(25.0, 0.0) 至 (52.0, 15.5)
- 禁止区域位置（9c）：
(25.0, 0.0) 至 (52.0, 15.5)
(25.0, 26.0) 至 (47.0, 40.0)

** 图 10
- 尺寸：35.0 x 25.3 米
- 起始姿态： (19.0, 20.0, 3.1416)
- 目标位置： (31.0, 5.0)
- 禁止区域位置：
(10.0, 0.0) 至 (35.0, 25.3)

** 图 11
- 尺寸：30.0 x 30.0 米
- 起始姿态： (7.0, 15.0, -1.5708)
- 目标位置： (5.0, 23.0)
- 禁止区域位置：
无

**** PPM 文件格式 ****
图像以 PPM 文件格式（ASCII）提供。这些文件具有以下结构：
# 行注释
...
<N_PIXELS_X> <N_PIXELS_Y>
<RGB_VALUE_1>
<RGB_VALUE_2>
<RGB_VALUE_3>
...
<RGB_VALUE_1>
<RGB_VALUE_2>
<RGB_VALUE_3>

此格式为：在注释行（以“#”开头）之后，前两个数字表示图像大小（以像素为单位），然后为每个 RGB 像素提供三个值。像素遵循行主顺序。R < 10；G < 10；B < 10 的像素被视为占用区域。其余为自由空间。
请注意，这些文件中的 <N_PIXELS_X> <N_PIXELS_Y> 与论文中环境的尺寸不符。每个像素都有一个坐标（X, Y），该坐标由图像分辨率和环境尺寸（DIM_X, DIM_Y）计算得出：
X = PIXEL_X * (DIM_X / N_PIXELS_X)
Y = PIXEL_Y * (DIM_Y / N_PIXELS_Y)

**** OT 文件格式 ****
与 Octomap[1] 库一起使用的文件也提供。这些文件使用 Octomap v.1.7.2 生成。这些文件已经包含了实验中使用的尺寸，无需额外处理。

[1] A. Hornung, K.M. Wurm, M. Bennewitz, C. Stachniss, 和 W. Burgard, “OctoMap：基于八叉树的效率化概率 3D 建图框架”发表于《Autonomous Robots》，2013；DOI：10.1007/s10514-012-9321-0。软件可在 http://octomap.github.com 获取。