24325f2a06
Major changes: - New square-root implementation for optimization and marginalization, giving faster optimization and numerically more stable marginalization. The square root solver is the new default, but the Schur complement based implementation is still available. (Implements the ICCV'21 paper.) - The odometry estimator is now fully templetized and you can run in float or double. Default is float, which works well with the new square-root implementation and gives best runtimes. - Batch evaluation scripts and documentation to reproduce the ICCV'21 experiments. Additional changes: - New options in VIO to marginalize lost landmark right away and not only when the frame is marginalized (enabled by default). - small bugfix for keypoint patch extraction bounds - basalt_vio: more logging for batch evaluation - basalt_vio: better handling of closing the GUI while estimator is still running - basalt_vio: new command line argument to limit the number of frames processed - basalt_vio: new command line argument to save ground truth trajectory - added unit tests for square root marginalization - update basalt-headers - new submodules: gmt, nlohmann/json, magic_enum
63 lines
1.4 KiB
Python
Executable File
63 lines
1.4 KiB
Python
Executable File
#!/usr/bin/env python3
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#
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# BSD 3-Clause License
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#
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# This file is part of the Basalt project.
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# https://gitlab.com/VladyslavUsenko/basalt.git
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#
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# Copyright (c) 2019-2021, Vladyslav Usenko and Nikolaus Demmel.
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# All rights reserved.
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#
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import os
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import sys
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import json
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datasets = ['Seq.', 'dataset-corridor1_512_16', 'dataset-magistrale1_512_16', 'dataset-room1_512_16', 'dataset-slides1_512_16']
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# Other results.
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vio = {
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'ate' : ['VIO RMS ATE [m]'],
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'time' : ['VIO Time [s]'],
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'num_frames' : ['VIO Num. Frames']
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}
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out_dir = sys.argv[1]
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def load_data(x, prefix, key):
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fname = out_dir + '/' + prefix + '_' + key
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if os.path.isfile(fname):
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with open(fname, 'r') as f:
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j = json.load(f)
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res = round(j['rms_ate'], 3)
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x['ate'].append(float(res))
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x['time'].append(round(j['exec_time_ns']*1e-9, 3))
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x['num_frames'].append(j['num_frames'])
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else:
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x['ate'].append(float('Inf'))
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x['time'].append(float('Inf'))
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x['num_frames'].append(float('Inf'))
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for key in datasets[1:]:
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load_data(vio, 'vio', key)
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row_format ="{:>17}" + "{:>13}" * (len(datasets)-1)
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datasets_short = [x[8:].split('_')[0] for x in datasets]
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print('\nVisual-Inertial Odometry')
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print(row_format.format(*datasets_short))
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print(row_format.format(*vio['ate']))
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#print(row_format.format(*vio['time']))
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print(row_format.format(*vio['num_frames']))
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