Minor virtual radar improvements

[HunterM321]

Added specific functions for radar cropping and fixed issues odom plotting behavior.

[Copilot]

Pull Request Overview

This PR refactors odometry plotting into a reusable function, enhances the sample teach‐submaps script with save options for point clouds, and adds a helper for radar‐based point cloud filtering.

• Extracts and centralizes 3D odometry path plotting into plot_odometry_path.
• Introduces --save_pc/--save_dir arguments and point‐cloud saving (individual & accumulated) in plot_teach_submaps.py.
• Adds pix4d2radar.py to filter point clouds based on a radar’s conic field of view.

Reviewed Changes

Copilot reviewed 3 out of 4 changed files in this pull request and generated 2 comments.

File	Description
src/vtr_odom_extractor/relative_export_odometry_path.py	Extract plot_odometry_path and replace duplicated inline odometry plotting
samples/plot_teach_submaps.py	Add CLI flags for saving point clouds and implement per‐cloud and accumulated writes
helpers/pix4d2radar.py	New script to load, filter, and save radar‐cropped point clouds based on angle & range

Comments suppressed due to low confidence (2)

src/vtr_odom_extractor/relative_export_odometry_path.py:12

• [nitpick] The newly introduced 'plot_odometry_path' function lacks unit or integration tests. Consider adding tests to validate the plotted boundary calculations and ensure it handles edge cases.

def plot_odometry_path(positions):

src/vtr_odom_extractor/relative_export_odometry_path.py:20

• The function uses 'np' without importing numpy in this module, which will cause a NameError. Please add 'import numpy as np' at the top of the file.

    max_range = np.array([positions[:,0].max()-positions[:,0].min(), 

[Copilot]

Appending 'new_points.T' transposes each point set to shape (3, N), which will misalign when vstacking later. Use 'new_points' (shape N×3) instead of its transpose.

Suggested change

	accumulated_points.append(new_points.T)
	accumulated_points.append(new_points)

[Copilot]

[nitpick] The script uses hardcoded file paths and parameters (e.g., input_ply, output_ply, base heights). Consider adding argparse support to make these values configurable and remove magic numbers for better flexibility.

Suggested change

	# Input and output file paths
	input_ply = "point_clouds/pin_4_crop_sub.ply"
	output_ply = "point_clouds/pin_4_filtered.ply"
	import argparse
	# Parse command-line arguments
	parser = argparse.ArgumentParser(description="Filter point cloud based on radar parameters.")
	parser.add_argument("--input_ply", type=str, default="point_clouds/pin_4_crop_sub.ply", help="Path to input PLY file.")
	parser.add_argument("--output_ply", type=str, default="point_clouds/pin_4_filtered.ply", help="Path to output PLY file.")
	parser.add_argument("--pix4d_pc_base_height", type=float, default=-12, help="Base height of the Pix4D point cloud.")
	parser.add_argument("--radar_height", type=float, default=1.5, help="Height of the radar above the base.")
	parser.add_argument("--radar_angle", type=float, default=2, help="Radar angle in degrees.")
	parser.add_argument("--radar_range", type=float, default=20, help="Radar range in meters.")
	args = parser.parse_args()
	# Input and output file paths
	input_ply = args.input_ply
	output_ply = args.output_ply