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update range locate code with new gwen(tm) algorithm

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Laura Klünder 2023-11-11 17:19:16 +01:00
parent 4dd79c238f
commit c3a9b178f7
1 changed files with 19 additions and 19 deletions
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36
src/c3nav/routing/rangelocator.py
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@ -1,6 +1,7 @@
import pickle
import threading
from dataclasses import dataclass
from pprint import pprint
from typing import Self
import numpy as np
@ -63,48 +64,45 @@ class RangeLocator:
cls.cached = cls.load_nocache(update)
return cls.cached
def locate(self, scan: dict[str, int], permissions=None):
def locate(self, ranges: dict[str, int], permissions=None):
# get the i and peer for every peer that we actually know
ranges = tuple(
(i, peer) for i, peer in (
(self.beacon_lookup.get(bssid, None), distance) for bssid, distance in scan.items()
relevant_ranges = tuple(
(i, distance) for i, distance in (
(self.beacon_lookup.get(bssid, None), distance) for bssid, distance in ranges.items()
) if i is not None
)
# create 2d array with x, y, z, distance as rows
np_ranges = np.hstack((
self.beacon_positions[tuple(i for i, distance in ranges), :],
np.array(tuple(distance for i, distance in ranges)).reshape((-1, 1)),
self.beacon_positions[tuple(i for i, distance in relevant_ranges), :],
np.array(tuple(distance for i, distance in relevant_ranges)).reshape((-1, 1)),
))
if np_ranges.shape[0] < 3:
# can't get a good result from just two beacons
# todo: maybe we can at least give… something?
print('less than 3 ranges, can\'t do ranging')
return None
if np_ranges.shape[0] == 3:
# TODO: three points aren't really enough for precise results? hm. maybe just a 2d fix then?
pass
print('2D trilateration')
dimensions = 2
else:
print('3D trilateration')
dimensions = 3
measured_ranges = np_ranges[:, 3]
measured_ranges = measured_ranges / np.max(measured_ranges)
# rating the guess by calculating the distances
def rate_guess(guess):
guessed_ranges = scipy.linalg.norm(np_ranges[:, :dimensions] - guess[:dimensions], axis=1)
guessed_ranges /= np.max(guessed_ranges)
diffs = guessed_ranges-measured_ranges
if (diffs < -200).any():
return diffs+100-np.clip(diffs, None, -200)*10
return diffs
def cost_func(guess):
factors = scipy.linalg.norm(np_ranges[:, :dimensions] - guess[:dimensions], axis=1) / measured_ranges
return factors - np.mean(factors)
# initial guess i the average of all beacons, with scale 1
initial_guess = np.average(np_ranges[:, :dimensions], axis=0)
# here the magic happens
results = least_squares(rate_guess, initial_guess)
results = least_squares(cost_func, initial_guess)
# create result
# todo: figure out level
@ -117,10 +115,12 @@ class RangeLocator:
icon='my_location'
)
pprint(relevant_ranges)
print("measured ranges:", ", ".join(("%.2f" % i) for i in tuple(np_ranges[:, 3])))
print("result ranges:", ", ".join(
("%.2f" % i) for i in tuple(scipy.linalg.norm(np_ranges[:, :dimensions] - results.x[:dimensions], axis=1))
))
print("cost:", cost_func(results.x))
if dimensions > 2:
print("height:", results.x[2])
# print("scale:", (factor or results.x[3]))