Source code for ouster.sdk.mapping.ouster_kiss_icp

"""
# MIT License
#
# Copyright (c) 2022 Ignacio Vizzo, Tiziano Guadagnino, Benedikt Mersch, Cyrill
# Stachniss.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
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# The above copyright notice and this permission notice shall be included in all
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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Module ouster_kiss_icp

Description:
    This module was a copy from the KissICP repository https://github.com/PRBonn/kiss-icp
    We edit it to fit our use cases better
"""


import numpy as np
import logging

from kiss_icp.config import KISSConfig  # type: ignore[import-untyped]
from kiss_icp.mapping import get_voxel_hash_map  # type: ignore[import-untyped]
from kiss_icp.registration import get_registration  # type: ignore[import-untyped]
from kiss_icp.threshold import get_threshold_estimator  # type: ignore[import-untyped]
from kiss_icp.voxelization import voxel_down_sample  # type: ignore[import-untyped]

import ouster.sdk.util.pose_util as pu
from ouster.sdk._bindings.mapping import _Preprocessor      # type: ignore[attr-defined]
from ouster.sdk._bindings.mapping import _Vector3dVector    # type: ignore[attr-defined]

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()


[docs]class Preprocessor: def __init__(self, max_range, min_range, deskew, max_num_threads): self._preprocessor = _Preprocessor( max_range, min_range, deskew, max_num_threads )
[docs] def preprocess(self, frame: np.ndarray, timestamps: np.ndarray, relative_motion: np.ndarray): return np.asarray( self._preprocessor._preprocess( _Vector3dVector(frame), timestamps.ravel(), relative_motion, ) )
[docs]class KissICP: def __init__(self, config: KISSConfig, initial_pose: np.ndarray = np.eye(4)): self.last_pose = initial_pose if initial_pose is not None else np.eye(4) self.last_delta_pose = np.eye(4) self.config = config self.adaptive_threshold = get_threshold_estimator(self.config) self.local_map = get_voxel_hash_map(self.config) self.registration = get_registration(self.config) self.preprocessor = Preprocessor(max_range=self.config.data.max_range, min_range=self.config.data.min_range, deskew=self.config.data.deskew, max_num_threads=0)
[docs] def register_frame(self, frame, timestamps, delta_ratio=1): # Preprocess the input cloud frame = self.preprocessor.preprocess(frame, timestamps, self.last_delta_pose) # Voxelize source, frame_downsample = self.voxelize(frame) sigma = self.adaptive_threshold.get_threshold() if delta_ratio == 1: initial_guess = self.last_pose @ self.last_delta_pose else: delta_pose_log = pu.log_pose(self.last_delta_pose) * delta_ratio delta_pose_exp = pu.exp_pose6(delta_pose_log) initial_guess = self.last_pose @ delta_pose_exp # Run ICP new_pose = self.registration.align_points_to_map( points=source, voxel_map=self.local_map, initial_guess=initial_guess, max_correspondance_distance=3 * sigma, kernel=sigma / 3, ) model_deviation = np.linalg.inv(initial_guess) @ new_pose self.adaptive_threshold.update_model_deviation(model_deviation) if delta_ratio == 1: self.last_delta_pose = np.linalg.inv(self.last_pose) @ new_pose elif delta_ratio <= 0: raise ValueError("frame delta_ratio must be greater than zero.") else: delta_pose_exp = np.linalg.inv(self.last_pose) @ new_pose delta_pose_log = pu.log_pose(delta_pose_exp) / delta_ratio self.last_delta_pose = pu.exp_pose6(delta_pose_log) self.last_pose = new_pose self.local_map.update(frame_downsample, new_pose)
[docs] def voxelize(self, iframe): frame_downsample = voxel_down_sample( iframe, self.config.mapping.voxel_size * 0.5) source = voxel_down_sample( frame_downsample, self.config.mapping.voxel_size * 1.5) return source, frame_downsample