1
0
mirror of https://github.com/opencv/opencv.git synced 2026-07-25 21:33:04 +04:00
Files
opencv/samples/slam/visual_odometry.cpp
T
2026-06-23 15:14:58 +05:30

84 lines
3.2 KiB
C++

// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
// Copyright (C) 2026, BigVision LLC, all rights reserved.
#include <opencv2/slam.hpp>
#include <opencv2/features.hpp>
#include <opencv2/dnn.hpp>
#include <opencv2/core.hpp>
#include <iostream>
using namespace cv;
static const char* keys =
"{ help h | | Print help message }"
"{ aliked | <none> | Path to ALIKED ONNX model }"
"{ lightglue | <none> | Path to LightGlue ONNX model }"
"{ images | <none> | Path to directory with input images }"
"{ output | vo_out | Output directory for trajectory and map }"
"{ fx | 718.856 | Camera focal length X }"
"{ fy | 718.856 | Camera focal length Y }"
"{ cx | 607.1928 | Camera principal point X }"
"{ cy | 185.2157 | Camera principal point Y }"
"{ min-parallax | 1.5 | Minimum initialisation parallax in degrees }"
"{ min-points | 50 | Minimum initialisation map points }";
int main(int argc, char** argv)
{
CommandLineParser parser(argc, argv, keys);
parser.about("Monocular visual odometry using ALIKED + LightGlue\n"
" Example: visual_odometry --aliked=aliked.onnx --lightglue=lg.onnx --images=./seq\n");
if (parser.has("help"))
{
parser.printMessage();
return 0;
}
const String alikedPath = parser.get<String>("aliked");
const String lightgluePath = parser.get<String>("lightglue");
const String imagesDir = parser.get<String>("images");
if (!parser.check() || alikedPath == "<none>" || lightgluePath == "<none>" || imagesDir == "<none>")
{
parser.printErrors();
parser.printMessage();
return 1;
}
const String outputDir = parser.get<String>("output");
const Matx33d K(parser.get<double>("fx"), 0., parser.get<double>("cx"),
0., parser.get<double>("fy"), parser.get<double>("cy"),
0., 0., 1.);
ALIKED::Params detParams;
detParams.inputSize = Size(640, 640);
detParams.engine = dnn::ENGINE_NEW;
auto detector = ALIKED::create(alikedPath, detParams);
auto matcher = LightGlueMatcher::create(lightgluePath, 0.0f,
dnn::DNN_BACKEND_DEFAULT,
dnn::DNN_TARGET_CPU);
slam::OdometryParams voParams;
voParams.minInitParallaxDeg = parser.get<double>("min-parallax");
voParams.minInitPoints = parser.get<int>("min-points");
auto vo = slam::VisualOdometry::create(
detector, matcher,
imagesDir, outputDir,
Mat(K), Mat(), voParams);
const int64 t0 = getTickCount();
const bool ok = vo->run();
const double elapsed = (getTickCount() - t0) / getTickFrequency();
std::cout << "run=" << (ok ? "ok" : "FAILED")
<< " frames=" << vo->getTrajectory().size()
<< " elapsed=" << elapsed << "s\n"
<< "output -> " << outputDir << "\n";
return ok ? 0 : 1;
}