File size: 7,151 Bytes
9befad5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
import numpy as np
from types import SimpleNamespace
from gpt6_b1k.visual_navigation import ObservedFloorGrid, forward_clearance, match_features, VisualTracker
from gpt6_b1k.sensors import Odometry


def test_reset_camera_frames_are_not_used_as_visual_anchors(monkeypatch):
    import io
    import gpt6_b1k.visual_navigation as nav
    policy = nav.VisualTrashPolicy(io.StringIO())
    obs = SimpleNamespace(kin=None, p=np.zeros(61), hold=lambda: np.ones(23))
    monkeypatch.setattr(nav, 'VisualSensors', lambda raw, kin: obs)
    updates = []
    monkeypatch.setattr(policy.tracker, 'update', lambda *args, **kw: updates.append(args[2]))
    policy.generator = iter([np.zeros(23)])
    for _ in range(10):
        action = policy.predict({})
        np.testing.assert_array_equal(action[:3], 0)
    assert updates == []
    policy.predict({})
    assert updates == [11]


def test_active_approach_receives_refined_object_position(monkeypatch):
    import io
    import gpt6_b1k.policy as base
    from gpt6_b1k.visual_navigation import VisualTrashPolicy
    from gpt6_b1k.perception import Detection
    policy = VisualTrashPolicy(io.StringIO())
    policy.obs = SimpleNamespace()
    policy.grid.observe = lambda *args: None
    target = np.array([1., 0., .07])
    policy.objects = [dict(point=target, done=False, attempts=0, seen=1)]
    detection = Detection(center=np.array([1.2, 0., .07]), kind='can',
        low=np.array([1.16, -.04, 0.]), high=np.array([1.24, .04, .14]),
        pixels=np.zeros((100, 2)), points=np.zeros((100, 3)), score=.8)
    monkeypatch.setattr(base, 'floor_objects', lambda obs: [detection])
    policy.observe()
    np.testing.assert_array_equal(target, detection.center)
    assert policy.objects[0]['point'] is target
    assert not policy.obs.strict_can_colors


def test_route_cannot_cross_unobserved_gap():
    grid = ObservedFloorGrid(size=80, resolution=.05)
    grid.seen_floor[10:30, 30:50] = True
    grid.seen_floor[45:65, 30:50] = True
    assert grid.route(grid.xy([20, 40]), grid.xy([55, 40])) is None
    grid.seen_floor[25:50, 30:50] = True
    grid._allowed = None
    assert grid.route(grid.xy([20, 40]), grid.xy([55, 40])) is not None


def test_furniture_clearance_blocks_too_narrow_passage():
    grid = ObservedFloorGrid(size=80, resolution=.05)
    grid.seen_floor[:] = True
    grid.occupied[:, 30] = True
    grid.occupied[:, 45] = True
    assert grid.route(grid.xy([20, 37]), grid.xy([60, 37])) is None


def test_observed_floor_clears_collected_can_but_keeps_table_above_it():
    grid, odom = ObservedFloorGrid(), Odometry()
    cloud = np.zeros((4, 4, 3))
    cloud[0, 0] = [1., 0., .12]
    cloud[2, 2] = [1., 1., .80]
    obs = SimpleNamespace(cloud=lambda: cloud, external=lambda points: np.ones(len(points), bool))
    grid.observe(obs, odom)
    can, table = tuple(grid.cell([1., 0.])), tuple(grid.cell([1., 1.]))
    assert grid.occupied[can] and grid.occupied[table]
    cloud[0, 0, 2] = cloud[2, 2, 2] = 0.
    grid.observe(obs, odom)
    assert not grid.occupied[can]
    assert grid.occupied[table]


def test_single_thin_leg_stops_forward_motion():
    xyz = np.full((8, 8, 3), 10.)
    xyz[2, 2] = [.39, .30, .10]
    obs = SimpleNamespace(cloud=lambda: xyz, external=lambda points: np.ones(len(points), bool))
    assert forward_clearance(obs) == .39


def test_short_sideways_alignment_checks_its_own_corridor():
    xyz = np.full((8, 8, 3), 10.)
    xyz[2, 2] = [.30, .39, .10]
    obs = SimpleNamespace(cloud=lambda: xyz, external=lambda points: np.ones(len(points), bool))
    assert forward_clearance(obs, direction=[0., 1.]) == .39


def test_depth_feature_match_recovers_observed_motion():
    rng = np.random.default_rng(4)
    points = rng.uniform([.6, -1., 0.], [3., 1., 1.], (80, 3))
    desc = rng.integers(0, 256, (80, 32), dtype=np.uint8)
    angle = .08
    rot = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])
    current = points.copy()
    current[:, :2] = current[:, :2]@rot.T + [.06, -.03]
    current[-15:, :2] += 1.  # Moving objects / incorrect depth must not set pose.
    tf, quality = match_features((points, desc), (current, desc))
    np.testing.assert_allclose(tf[:2, :2], rot, atol=.001)
    np.testing.assert_allclose(tf[:2, 3], [.06, -.03], atol=.001)
    assert quality['inliers'] >= 60


def test_failed_visual_match_retains_trusted_anchor(monkeypatch):
    import gpt6_b1k.visual_navigation as nav
    empty = (np.empty((0, 3)), None)
    monkeypatch.setattr(nav, 'visual_features', lambda obs: empty)
    tracker, odom = VisualTracker(), Odometry()
    obs = SimpleNamespace(p=np.zeros(61))
    tracker.update(obs, odom, 1)
    anchor = tracker.anchor
    tracker.update(obs, odom, 5)
    assert tracker.anchor is anchor and tracker.last_match == 1


def test_official_camera_calibration_order_and_privileged_filter():
    from gpt6_b1k.visual_navigation import VisualSensors
    p = np.zeros(61)
    p[23] = p[48] = 1
    poses = np.array([[1., 0, 0, 0, 0, 0, 1], [2., 0, 0, 0, 0, 0, 1], [3., 0, 0, 0, 0, 0, 1]])
    obs = VisualSensors({'robot::proprio': p, 'robot::cam_rel_poses': poses.ravel(),
                         'object_poses': {'forbidden': [99, 99, 99]}, 'q_score': 1.})
    for i, camera in enumerate(('left_realsense', 'right_realsense', 'zed'), 1):
        np.testing.assert_array_equal(obs.camera(camera)[:3, 3], [i, 0, 0])
    assert 'object_poses' not in obs.raw and 'q_score' not in obs.raw


def test_navigation_reaches_goal_between_grid_cells():
    import io
    from gpt6_b1k.visual_navigation import VisualTrashPolicy
    from gpt6_b1k.sensors import planar
    policy = VisualTrashPolicy(io.StringIO())
    policy.odom.pose = np.array([.05, .05, np.pi/4])
    policy.grid.seen_floor[:] = True
    cloud = np.tile([2., 0., 0.], (8, 8, 1))
    policy.obs = SimpleNamespace(hold=lambda: np.zeros(23), cloud=lambda: cloud,
        external=lambda points: np.ones(len(points), bool))
    policy.observe = lambda: None
    goal = np.array([.125, .125, np.pi/4])
    # Its rounded endpoint (.10,.10) is already within the waypoint tolerance,
    # although the actual goal is still outside the route completion tolerance.
    for action in policy.travel(goal):
        policy.steps += 1
        policy.tracker.last_match = policy.steps
        rotation = planar(policy.odom.pose)[:2, :2]
        policy.odom.pose[:2] += rotation@action[:2]/30*.75
        policy.odom.pose[2] += action[2]/30
        assert policy.steps < 200
    assert np.linalg.norm(policy.odom.pose[:2]-goal[:2]) < .10


def test_discovery_rejects_wood_but_keeps_rich_orange_can():
    from gpt6_b1k.perception import floor_objects
    x, y = np.meshgrid(np.linspace(.70, .77, 20), np.linspace(-.30, -.23, 20))
    cloud = np.stack((x, y, np.linspace(.025, .135, 400).reshape(20, 20)), -1)
    rgb = np.tile([150, 110, 75], (20, 20, 1))
    obs = SimpleNamespace(cloud=lambda camera: cloud, get=lambda camera, modality: rgb,
                          eef=lambda arm: np.array([-2., 0., 0.]), strict_can_colors=True)
    assert not floor_objects(obs)
    rgb[:] = [220, 95, 12]
    assert len(floor_objects(obs)) == 1