This forks

1. appended which human is visible or not
2. appended Pure-Pursuit (reference code: repo )
3. appended Occupancy grid map (reference code: repo )

1. Add which agent is visibility or not

e.g. (in CrowdNav[https://github.com/vita-epfl/CrowdNav])

class MYClass():
    def __init__(self):
        pass

    def sort_humans(
        self,
        robot: Robot,
        humans: List[Human]
        ):
                ## sorting human by descending_order for make Occupancy 
        distances = []
        for human in humans:
            dist = np.linalg.norm([human.px- robot.px, human.py - robot.py])
            distances.append(dist)
        descending_order = np.array(distances).argsort()
        humans = np.array(humans)[descending_order]
        humans = humans.tolist()
        return humans 

    def _cb_lidar(
        self, 
        robot: Robot, 
        humans: List[Human,], 
        flat_contours: np.ndarray,
        distances_travelled_in_base_frame: np.ndarray
        ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, List[bool]]:
        humans = self.sort_humans(robot,humans)
        lidar_pos = np.array([robot.px, robot.py, robot.theta], dtype=np.float32)
        ranges = np.ones((self.n_angles,), dtype=np.float32) * self.max_range
        angles = np.linspace(self.scan_min_angle,
                             self.scan_max_angle-self.scan_increment,
                             self.n_angles) + lidar_pos[2]
        other_agents = []
        for i, human in enumerate(humans):
            pos = np.array([human.px, human.py, human.theta], dtype=np.float32)
            dist = distances_travelled_in_base_frame[i].astype(np.float32)
            vel = np.array([human.vx, human.vy], dtype=np.float32)
            if self.lidar_legs:
                agent = CSimAgent(pos, dist, vel, type_="legs", radius = self.leg_radius)
            else:
                agent = CSimAgent(pos, dist, vel, type_="trunk", radius=human.radius)
            other_agents.append(agent)

        self.converter_cmap2d.render_agents_in_lidar(ranges, angles, other_agents, lidar_pos[:2])

        which_visible = [agent.get_agent_which_visible() for agent in other_agents] or which_visible = [agent.visible for agent in other_agent]
        return ranges, angles, distances_travelled_in_base_frame, which_visible

Notion

get_agent_which_visible or visible must be called after render_agents_in_lidar

sort_humans must be used, before input humans into render_agents_in_lidar (i recommend that you should use sort_humans human step and human generate function)

2. Add subgoal planner (Called Pure-Pursuit)

class MYClass():
    def __init__(self):
        self.look_ahead_planner = LookAheadPlanner(look_ahead_dist = 2.5)

    def _cb_subgoal(self, robot: Robot, path:np.ndarray):
        path = path.astype(np.float32)
        position = np.array([robot.px, robot.py], dtype = np.float32)
        sub_goal = self.look_ahead_planner.find_subgoal(path, position)
        return sub_goal 

3. Add Occupancy Grid Map (Local Map)

OGMs.mp4

class MYClass():
    def __init__(self):
        self.occ_map = OccupancyGridMap(xy_resolution = 0.05, map_size = 11.2)

    def lidar_to_occ(self, ranges: np.ndarray, angles: np.ndarray) -> np.ndarray:
        occ = self.occ_map.generate_ray_casting_grid_map(ranges.astype(np.double), angles.astype(np.double))
        return occ

    def visualize_occ(self, ranges: np.ndarray , angles: np.ndarray) -> np.ndarray:
        occ = self.lidar_to_occ(ranges, angles)
        cv2.imshow('occ_map', occ)
        cv2.waitkey(1)

4. Add Occupancy Grid Map with detector (Local Map)

apply pedestrian detector

If when you want to use this in real env, using this repo

class MYClass():
    def __init__(self):
        self.occ_map = OccupancyGridMap(xy_resolution = 0.05, map_size = 11.2, using_detector = True, detect_value = 200.0)

    def lidar_to_occ(self, ranges: np.ndarray, angles: np.ndarray, visible_indices: np.ndarray ) -> np.ndarray:
        occ = self.occ_map.generate_ray_casting_grid_map(ranges.astype(np.double), angles.astype(np.double), visible_indices.astype(np.bool_)
        return occ

    def visualize_occ(self, ranges: np.ndarray , angles: np.ndarray) -> np.ndarray:
        occ = self.lidar_to_occ(ranges, angles)
        cv2.imshow('occ_map', occ)
        cv2.waitkey(1)

pymap2d

pymap2d is a Cython-based fast toolbox for 2d grid maps.

The CMap2D class provides:

• simple xy <-> ij coordinate conversions
• implementation of the dijkstra / fastmarch algorithm
• fast 2D distance transform (ESDF)
• conversions:
  • to/from polygon vertices
  • from ROS occupancy map or lidar scan message
  • serialization to/from dict

Note: rather than carefully designed, this codebase was chaotically grown. It is in dire need of refactoring / documentation. I hope it still proves useful.

Dependency: Cython

$ pip3 install numpy==1.23.4 Cython==0.29.37

Installation:

Inside this project root folder:

$ pip3 install -e .

How to

Creating a map

from CMap2D import CMap2D

# empty map
mymap = CMap2D()

# from an array
mymap.from_array(array, origin, resolution)

# from a pgm file
mymap = CMap2D("folder", "filename")

# from a ROS message
mymap.from_msg(msg)

Accessing occupancy data, origin, resolution (read-only)

# occupancy as 2d array
mymap.occupancy()

# origin: (x, y) coordinates of point (i, j) = (0, 0)
mymap.origin_xy()

# resolution: size of grid cell [meters]
mymap.resolution()

Converting between grid and spatial coordinates

list_of_xy_points = np.array([[1.3, 2.3], [-1.1, -4.0], [6.4, 2.3]])

in_ij_coordinates = mymap.xy_to_floatij(list_of_xy_points)
as_indices = mymap.xy_to_ij(list_of_xy_points, clip_if_outside=True)

gridshow is a convenience function, which wraps plt.imshow to intuitively visualize 2d array contents. It makes the first array dimension x axis and uses grayscale by default.

from CMap2D import gridshow
gridshow(mymap.occupancy())

For more examples, see test/example_*.py