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Updated test to catch #1714

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matlabbe
2026-06-07 14:45:25 -07:00
parent 41ed101462
commit 79408c8371
1 changed files with 40 additions and 3 deletions
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corelib/test/test_rtabmap_integration.cpp
+40 -3
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@@ -22,6 +22,7 @@
#include <rtabmap/core/OccupancyGrid.h>
#include <rtabmap/core/Odometry.h>
#include <rtabmap/core/Optimizer.h>
#include <rtabmap/core/Memory.h>
#include <rtabmap/core/Signature.h>
#include <rtabmap/core/Version.h>
#ifdef RTABMAP_OCTOMAP
@@ -2055,8 +2056,8 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
params[Parameters::kSURFHessianThreshold()] = "150";
params[Parameters::kMemSTMSize()] = "20";
params[Parameters::kKpTfIdfLikelihoodUsed()] = tfIdfUsed ? "true" : "false";
params[Parameters::kKpMaxFeatures()] = "400";
params[Parameters::kKpBadSignRatio()] = "0.1";
params[Parameters::kKpMaxFeatures()] = "500";
params[Parameters::kKpBadSignRatio()] = "0.25";
// SIFT-specific: lower the contrast threshold so more keypoints
// survive on the low-texture frames in data/samples.
params[Parameters::kSIFTContrastThreshold()] = "0.01";
@@ -2069,7 +2070,7 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
// GFTT-specific: tighten the minimum keypoint separation (default
// 7 px) so more candidates fit per frame.
params[Parameters::kGFTTMinDistance()] = "5";
params[Parameters::kMemBadSignaturesIgnored()] = "true";
params[Parameters::kMemBadSignaturesIgnored()] = "false";
params[Parameters::kMemRehearsalSimilarity()] = "0.20";
// Backend-specific asset paths + GPU/CUDA toggle. `useGpu` only
@@ -2125,6 +2126,7 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
UTimer wall;
int i = 0;
int badSignatureCount = 0;
SensorData data = camera.takeImage();
while(!data.imageRaw().empty())
{
@@ -2134,6 +2136,27 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
const bool ok = rtabmap.process(data, Transform());
ASSERT_TRUE(ok) << detectorLabel << " rtabmap.process failed at frame " << i;
// Per-frame checks on the just-processed signature in
// working memory:
// * verify keypoints and descriptors stay 1-to-1 (no row
// silently dropped during BOW quantization),
// * count bad signatures (low-texture frames on this
// 84-image set produce a handful with
// Mem/BadSignaturesIgnored=false; rehearsal does not
// merge bad signatures, so the per-frame count is stable).
const Signature * lastSig =
rtabmap.getMemory()->getLastWorkingSignature(false);
ASSERT_TRUE(lastSig != nullptr)
<< detectorLabel << " frame " << i << ": no last signature";
EXPECT_EQ(static_cast<int>(lastSig->getWordsKpts().size()),
lastSig->getWordsDescriptors().rows)
<< detectorLabel << " frame " << i
<< " (id=" << lastSig->id() << "): keypoints/descriptors "
<< "size mismatch on signature ("
<< lastSig->getWordsKpts().size() << " vs "
<< lastSig->getWordsDescriptors().rows << ")";
if(lastSig->isBadSignature()) ++badSignatureCount;
FrameStat s;
s.queryRow = i - 1;
s.hypValue = rtabmap.getHighestHypothesisValue();
@@ -2162,6 +2185,19 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
ASSERT_EQ(kNumFrames, i)
<< detectorLabel << " expected " << kNumFrames
<< " frames from " << samplesDir << ", got " << i;
// Mem/BadSignaturesIgnored=false should reveal at least one bad
// signature during the 84-frame run -- except for SuperPoint
// (Magicleap) on GPU, whose dense features always yield >= 1
// valid word per frame on this dataset, so no frame ever turns
// into a bad signature.
if(detectorType != Feature2D::kFeatureSuperPointTorch)
{
EXPECT_GE(badSignatureCount, 1)
<< detectorLabel << " expected at least 1 bad signature with "
<< "Mem/BadSignaturesIgnored=false; got " << badSignatureCount;
}
rtabmap.close();
// Standard rtabmap P/R curve: sort frames by hypothesis value
@@ -2277,6 +2313,7 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
}
const bool isCornerBased =
detectorType == Feature2D::kFeatureOrb ||
detectorType == Feature2D::kFeatureGfttFreak ||
detectorType == Feature2D::kFeatureGfttBrief ||
detectorType == Feature2D::kFeatureGfttOrb ||