{"id":87618,"date":"2026-07-30T15:27:05","date_gmt":"2026-07-30T07:27:05","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/87618.html"},"modified":"2026-07-30T15:27:05","modified_gmt":"2026-07-30T07:27:05","slug":"orb-slam3%e8%a7%86%e8%a7%89slam%e5%8e%9f%e7%90%86%e4%b8%8eros2%e5%ae%9e%e6%88%98","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/87618.html","title":{"rendered":"ORB-SLAM3\u89c6\u89c9SLAM\u539f\u7406\u4e0eROS2\u5b9e\u6218"},"content":{"rendered":"

ORB-SLAM3\u89c6\u89c9SLAM\u539f\u7406\u4e0eROS2\u5b9e\u6218<\/h2>\n

\u4e00\u3001\u5f15\u8a00<\/h3>\n

SLAM(\u540c\u65f6\u5b9a\u4f4d\u4e0e\u5efa\u56fe)\u662f\u673a\u5668\u4eba\u548cAR\u7684\u6838\u5fc3\u3002ORB-SLAM3\u662f\u89c6\u89c9SLAM\u7684SOTA\u65b9\u6848，\u652f\u6301\u5355\u76ee\/\u53cc\u76ee\/RGB-D+IMU\u878d\u5408，\u80fd\u5728\u52a8\u6001\u73af\u5883\u4e2d\u9c81\u68d2\u8fd0\u884c\u3002\u672c\u6587\u5c06\u89e3\u6790\u5176\u4e09\u5927\u7ebf\u7a0b\u5e76\u7ed9\u51faROS2\u96c6\u6210\u3002<\/p>\n

\u4e8c\u3001\u4e09\u7ebf\u7a0b\u67b6\u6784<\/h3>\n

\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u5e27 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u5173\u952e\u5e27 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n\u2502 Tracking \u2502\u2500\u2500\u2500\u2500\u2500\u2192\u2502 Local Mapping\u2502\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2192\u2502 Loop Closing \u2502
\n\u2502 (\u5b9e\u65f6\u8ddf\u8e2a) \u2502 \u2502 (\u5c40\u90e8\u5efa\u56fe) \u2502 \u2502 (\u56de\u73af\u68c0\u6d4b) \u2502
\n\u2502 \u2502\u2190\u2500\u2500\u2500\u2500\u2500\u2502 \u2502 \u2502 \u2502
\n\u2502 ORB\u63d0\u53d6+\u5339\u914d \u2502 \u2502 BA\u4f18\u5316 \u2502 \u2502 \u8bcd\u888b\u68c0\u6d4b \u2502
\n\u2502 \u4f4d\u59ff\u4f30\u8ba1 \u2502 \u2502 \u65b0\u5730\u56fe\u70b9\u751f\u6210 \u2502 \u2502 \u5168\u5c40BA \u2502
\n\u2502 \u91cd\u5b9a\u4f4d \u2502 \u2502 \u5197\u4f59\u5e27\u5254\u9664 \u2502 \u2502 \u4f4d\u59ff\u56fe\u4f18\u5316 \u2502
\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518<\/p>\n

\u4e09\u3001ORB\u7279\u5f81\u63d0\u53d6<\/h3>\n

import<\/span> cv2
\nimport<\/span> numpy as<\/span> np<\/p>\n

class<\/span> ORBExtractor<\/span>:<\/span>
\n def<\/span> __init__<\/span>(<\/span>self,<\/span> n_features=<\/span>1000<\/span>,<\/span> scale_factor=<\/span>1.2<\/span>,<\/span> n_levels=<\/span>8<\/span>)<\/span>:<\/span>
\n self.<\/span>orb =<\/span> cv2.<\/span>ORB_create(<\/span>
\n nfeatures=<\/span>n_features,<\/span>
\n scaleFactor=<\/span>scale_factor,<\/span>
\n nlevels=<\/span>n_levels,<\/span>
\n edgeThreshold=<\/span>31<\/span>,<\/span>
\n patchSize=<\/span>31<\/span>,<\/span>
\n fastThreshold=<\/span>20<\/span>
\n )<\/span><\/p>\n

def<\/span> extract<\/span>(<\/span>self,<\/span> image)<\/span>:<\/span>
\n """\u63d0\u53d6ORB\u7279\u5f81\u70b9+\u63cf\u8ff0\u5b50"""<\/span>
\n keypoints,<\/span> descriptors =<\/span> self.<\/span>orb.<\/span>detectAndCompute(<\/span>image,<\/span> None<\/span>)<\/span><\/p>\n

# \u56db\u53c9\u6811\u5747\u5300\u5206\u5e03（ORB-SLAM\u72ec\u6709，\u907f\u514d\u7279\u5f81\u70b9\u805a\u96c6）<\/span>
\n keypoints =<\/span> self.<\/span>_distribute_quadtree(<\/span>keypoints,<\/span> image.<\/span>shape)<\/span><\/p>\n

return<\/span> keypoints,<\/span> descriptors<\/p>\n

def<\/span> _distribute_quadtree<\/span>(<\/span>self,<\/span> kps,<\/span> shape,<\/span> min_nodes=<\/span>4<\/span>)<\/span>:<\/span>
\n """\u56db\u53c9\u6811\u5747\u5300\u5206\u5e03\u7279\u5f81\u70b9"""<\/span>
\n h,<\/span> w =<\/span> shape[<\/span>:<\/span>2<\/span>]<\/span>
\n nodes =<\/span> [<\/span>Node(<\/span>0<\/span>,<\/span> 0<\/span>,<\/span> w,<\/span> h,<\/span> kps)<\/span>]<\/span><\/p>\n

while<\/span> len<\/span>(<\/span>nodes)<\/span> <<\/span> min_nodes:<\/span>
\n new_nodes =<\/span> [<\/span>]<\/span>
\n for<\/span> node in<\/span> nodes:<\/span>
\n if<\/span> len<\/span>(<\/span>node.<\/span>kps)<\/span> <=<\/span> 1<\/span>:<\/span>
\n new_nodes.<\/span>append(<\/span>node)<\/span>
\n else<\/span>:<\/span>
\n n1,<\/span> n2,<\/span> n3,<\/span> n4 =<\/span> node.<\/span>split(<\/span>)<\/span>
\n new_nodes.<\/span>extend(<\/span>[<\/span>n1,<\/span> n2,<\/span> n3,<\/span> n4]<\/span>)<\/span>
\n nodes =<\/span> new_nodes<\/p>\n

# \u6bcf\u4e2a\u8282\u70b9\u4fdd\u7559\u6700\u5f3a\u54cd\u5e94\u70b9<\/span>
\n result =<\/span> [<\/span>]<\/span>
\n for<\/span> node in<\/span> nodes:<\/span>
\n if<\/span> node.<\/span>kps:<\/span>
\n best =<\/span> max<\/span>(<\/span>node.<\/span>kps,<\/span> key=<\/span>lambda<\/span> k:<\/span> k.<\/span>response)<\/span>
\n result.<\/span>append(<\/span>best)<\/span>
\n return<\/span> result<\/p>\n

\u56db\u3001PnP\u4f4d\u59ff\u4f30\u8ba1<\/h3>\n

def<\/span> estimate_pose_ransac<\/span>(<\/span>pts_3d,<\/span> pts_2d,<\/span> K)<\/span>:<\/span>
\n """EPnP + RANSAC: 3D-2D\u5339\u914d\u4f30\u8ba1\u76f8\u673a\u4f4d\u59ff"""<\/span>
\n # \u964d\u7ea7\u65b9\u68481: \u75283D-2D PnP<\/span>
\n success,<\/span> rvec,<\/span> tvec,<\/span> inliers =<\/span> cv2.<\/span>solvePnPRansac(<\/span>
\n pts_3d,<\/span> pts_2d,<\/span> K,<\/span> None<\/span>,<\/span>
\n iterationsCount=<\/span>100<\/span>,<\/span>
\n reprojectionError=<\/span>4.0<\/span>,<\/span>
\n confidence=<\/span>0.99<\/span>,<\/span>
\n flags=<\/span>cv2.<\/span>SOLVEPNP_EPNP
\n )<\/span><\/p>\n

if<\/span> success:<\/span>
\n R,<\/span> _ =<\/span> cv2.<\/span>Rodrigues(<\/span>rvec)<\/span>
\n T =<\/span> np.<\/span>eye(<\/span>4<\/span>)<\/span>;<\/span> T[<\/span>:<\/span>3<\/span>,<\/span> :<\/span>3<\/span>]<\/span> =<\/span> R;<\/span> T[<\/span>:<\/span>3<\/span>,<\/span> 3<\/span>]<\/span> =<\/span> tvec.<\/span>flatten(<\/span>)<\/span>
\n return<\/span> T,<\/span> len<\/span>(<\/span>inliers)<\/span><\/p>\n

# \u964d\u7ea7\u65b9\u68482: \u5355\u5e94\u77e9\u9635(\u5e73\u9762\u573a\u666f)<\/span>
\n if<\/span> len<\/span>(<\/span>pts_2d)<\/span> >=<\/span> 4<\/span>:<\/span>
\n H,<\/span> mask =<\/span> cv2.<\/span>findHomography(<\/span>pts_3d[<\/span>:<\/span>,<\/span> :<\/span>2<\/span>]<\/span>,<\/span> pts_2d,<\/span> cv2.<\/span>RANSAC,<\/span> 4.0<\/span>)<\/span>
\n if<\/span> H is<\/span> not<\/span> None<\/span>:<\/span>
\n # \u4eceH\u5206\u89e3\u4f4d\u59ff<\/span>
\n solutions =<\/span> cv2.<\/span>decomposeHomographyMat(<\/span>H,<\/span> K)<\/span>
\n return<\/span> solutions[<\/span>0<\/span>]<\/span>,<\/span> mask.<\/span>sum<\/span>(<\/span>)<\/span><\/p>\n

return<\/span> None<\/span>,<\/span> 0<\/span><\/p>\n

\u4e94\u3001\u5c40\u90e8BA\u4f18\u5316<\/h3>\n

import<\/span> g2o<\/p>\n

class<\/span> BundleAdjustment<\/span>:<\/span>
\n def<\/span> optimize<\/span>(<\/span>self,<\/span> keyframes,<\/span> mappoints,<\/span> fixed_kf_id)<\/span>:<\/span>
\n optimizer =<\/span> g2o.<\/span>SparseOptimizer(<\/span>)<\/span>
\n solver =<\/span> g2o.<\/span>BlockSolverSE3(<\/span>g2o.<\/span>LinearSolverEigenSE3(<\/span>)<\/span>)<\/span>
\n optimizer.<\/span>set_algorithm(<\/span>g2o.<\/span>OptimizationAlgorithmLevenberg(<\/span>solver)<\/span>)<\/span><\/p>\n

# \u6dfb\u52a0\u76f8\u673a\u9876\u70b9<\/span>
\n kf_vertices =<\/span> {<\/span>}<\/span>
\n for<\/span> kf in<\/span> keyframes:<\/span>
\n v =<\/span> g2o.<\/span>VertexSE3Expmap(<\/span>)<\/span>
\n v.<\/span>set_id(<\/span>kf.<\/span>id<\/span>)<\/span>
\n v.<\/span>set_estimate(<\/span>g2o.<\/span>SE3Quat(<\/span>kf.<\/span>R,<\/span> kf.<\/span>t)<\/span>)<\/span>
\n v.<\/span>set_fixed(<\/span>kf.<\/span>id<\/span> ==<\/span> fixed_kf_id)<\/span>
\n optimizer.<\/span>add_vertex(<\/span>v)<\/span>
\n kf_vertices[<\/span>kf.<\/span>id<\/span>]<\/span> =<\/span> v<\/p>\n

# \u6dfb\u52a0\u5730\u56fe\u70b9\u9876\u70b9<\/span>
\n for<\/span> mp in<\/span> mappoints:<\/span>
\n v =<\/span> g2o.<\/span>VertexPointXYZ(<\/span>)<\/span>
\n v.<\/span>set_id(<\/span>mp.<\/span>id<\/span> +<\/span> 10000<\/span>)<\/span>
\n v.<\/span>set_estimate(<\/span>mp.<\/span>position)<\/span>
\n v.<\/span>set_marginalized(<\/span>True<\/span>)<\/span>
\n optimizer.<\/span>add_vertex(<\/span>v)<\/span><\/p>\n

# \u6dfb\u52a0\u91cd\u6295\u5f71\u8fb9<\/span>
\n for<\/span> kf in<\/span> keyframes:<\/span>
\n for<\/span> mp_id,<\/span> (<\/span>kp,<\/span> _)<\/span> in<\/span> kf.<\/span>observations.<\/span>items(<\/span>)<\/span>:<\/span>
\n if<\/span> mp_id in<\/span> mappoints:<\/span>
\n edge =<\/span> g2o.<\/span>EdgeSE3ProjectXYZ(<\/span>)<\/span>
\n edge.<\/span>set_vertex(<\/span>0<\/span>,<\/span> kf_vertices[<\/span>kf.<\/span>id<\/span>]<\/span>)<\/span>
\n edge.<\/span>set_vertex(<\/span>1<\/span>,<\/span> optimizer.<\/span>vertex(<\/span>mp_id +<\/span> 10000<\/span>)<\/span>)<\/span>
\n edge.<\/span>set_measurement(<\/span>kp.<\/span>pt)<\/span>
\n edge.<\/span>set_information(<\/span>np.<\/span>eye(<\/span>2<\/span>)<\/span>)<\/span>
\n edge.<\/span>set_parameter_id(<\/span>0<\/span>,<\/span> K)<\/span> # \u5185\u53c2<\/span>
\n optimizer.<\/span>add_edge(<\/span>edge)<\/span><\/p>\n

optimizer.<\/span>initialize_optimization(<\/span>)<\/span>
\n optimizer.<\/span>optimize(<\/span>10<\/span>)<\/span><\/p>\n

# \u66f4\u65b0\u4f4d\u59ff\u548c\u5730\u56fe\u70b9<\/span>
\n for<\/span> kf in<\/span> keyframes:<\/span>
\n kf.<\/span>set_pose(<\/span>kf_vertices[<\/span>kf.<\/span>id<\/span>]<\/span>.<\/span>estimate(<\/span>)<\/span>)<\/span><\/p>\n

\u516d\u3001\u56de\u73af\u68c0\u6d4b(DBoW2\u8bcd\u888b)<\/h3>\n

# \u8bcd\u888b\u6a21\u578b：ORB\u63cf\u8ff0\u5b50\u2192\u89c6\u89c9\u8bcd\u6c47\u2192BoW\u5411\u91cf<\/span>
\n# \u76f8\u4f3c\u5ea6>\u9608\u503c \u2192 \u56de\u73af\u5019\u9009 \u2192 Sim3\u9a8c\u8bc1 \u2192 \u4f4d\u59ff\u56fe\u4f18\u5316<\/span><\/p>\n

class<\/span> LoopDetector<\/span>:<\/span>
\n def<\/span> __init__<\/span>(<\/span>self,<\/span> vocabulary_path)<\/span>:<\/span>
\n self.<\/span>vocab =<\/span> cv2.<\/span>BOWKMeansTrainer.<\/span>load(<\/span>vocabulary_path)<\/span><\/p>\n

def<\/span> detect<\/span>(<\/span>self,<\/span> current_frame,<\/span> all_keyframes,<\/span> min_score=<\/span>0.3<\/span>)<\/span>:<\/span>
\n # \u7f16\u7801\u5f53\u524d\u5e27<\/span>
\n current_bow =<\/span> self.<\/span>_compute_bow(<\/span>current_frame.<\/span>descriptors)<\/span><\/p>\n

# \u4e0e\u6240\u6709\u5386\u53f2\u5173\u952e\u5e27\u6bd4\u8f83<\/span>
\n candidates =<\/span> [<\/span>]<\/span>
\n for<\/span> kf in<\/span> all_keyframes:<\/span>
\n score =<\/span> self.<\/span>_bow_similarity(<\/span>current_bow,<\/span> kf.<\/span>bow)<\/span>
\n if<\/span> score ><\/span> min_score:<\/span>
\n candidates.<\/span>append(<\/span>(<\/span>kf,<\/span> score)<\/span>)<\/span><\/p>\n

# \u7ec4\u4e00\u81f4\u6027\u68c0\u67e5(\u8fde\u7eed3\u5e27\u68c0\u6d4b\u5230\u540c\u7ec4)<\/span>
\n groups =<\/span> self.<\/span>_group_consistency(<\/span>candidates)<\/span><\/p>\n

# \u51e0\u4f55\u9a8c\u8bc1(Sim3)<\/span>
\n for<\/span> group in<\/span> groups:<\/span>
\n best_kf =<\/span> max<\/span>(<\/span>group,<\/span> key=<\/span>lambda<\/span> x:<\/span> x[<\/span>1<\/span>]<\/span>)<\/span>[<\/span>0<\/span>]<\/span>
\n # 3D-3D RANSAC\u9a8c\u8bc1<\/span>
\n if<\/span> self.<\/span>_geometric_verification(<\/span>current_frame,<\/span> best_kf)<\/span>:<\/span>
\n return<\/span> best_kf<\/p>\n

return<\/span> None<\/span><\/p>\n

\u4e03\u3001ROS2\u96c6\u6210<\/h3>\n

import<\/span> rclpy
\nfrom<\/span> rclpy.<\/span>node import<\/span> Node
\nfrom<\/span> sensor_msgs.<\/span>msg import<\/span> Image
\nfrom<\/span> geometry_msgs.<\/span>msg import<\/span> PoseStamped<\/p>\n

class<\/span> SLAMNode<\/span>(<\/span>Node)<\/span>:<\/span>
\n def<\/span> __init__<\/span>(<\/span>self)<\/span>:<\/span>
\n super<\/span>(<\/span>)<\/span>.<\/span>__init__(<\/span>'orb_slam3'<\/span>)<\/span>
\n self.<\/span>slam =<\/span> ORBSLAM3(<\/span>"\/path\/to\/vocabulary.txt"<\/span>,<\/span> "\/path\/to\/config.yaml"<\/span>)<\/span><\/p>\n

self.<\/span>create_subscription(<\/span>Image,<\/span> '\/camera\/color'<\/span>,<\/span> self.<\/span>rgb_callback,<\/span> 10<\/span>)<\/span>
\n self.<\/span>create_subscription(<\/span>Image,<\/span> '\/camera\/depth'<\/span>,<\/span> self.<\/span>depth_callback,<\/span> 10<\/span>)<\/span>
\n self.<\/span>pose_pub =<\/span> self.<\/span>create_publisher(<\/span>PoseStamped,<\/span> '\/slam\/pose'<\/span>,<\/span> 10<\/span>)<\/span><\/p>\n

def<\/span> rgb_callback<\/span>(<\/span>self,<\/span> msg)<\/span>:<\/span>
\n cv_image =<\/span> self.<\/span>bridge.<\/span>imgmsg_to_cv2(<\/span>msg,<\/span> "bgr8"<\/span>)<\/span>
\n timestamp =<\/span> msg.<\/span>header.<\/span>stamp.<\/span>sec +<\/span> msg.<\/span>header.<\/span>stamp.<\/span>nanosec *<\/span> 1e-9<\/span><\/p>\n

pose =<\/span> self.<\/span>slam.<\/span>track_rgbd(<\/span>cv_image,<\/span> self.<\/span>last_depth,<\/span> timestamp)<\/span><\/p>\n

if<\/span> pose is<\/span> not<\/span> None<\/span>:<\/span>
\n pose_msg =<\/span> PoseStamped(<\/span>)<\/span>
\n pose_msg.<\/span>header =<\/span> msg.<\/span>header
\n pose_msg.<\/span>pose.<\/span>position.<\/span>x =<\/span> pose[<\/span>0<\/span>,<\/span> 3<\/span>]<\/span>
\n pose_msg.<\/span>pose.<\/span>position.<\/span>y =<\/span> pose[<\/span>1<\/span>,<\/span> 3<\/span>]<\/span>
\n pose_msg.<\/span>pose.<\/span>position.<\/span>z =<\/span> pose[<\/span>2<\/span>,<\/span> 3<\/span>]<\/span>
\n self.<\/span>pose_pub.<\/span>publish(<\/span>pose_msg)<\/span><\/p>\n

\u516b\u3001\u603b\u7ed3<\/h3>\n

ORB-SLAM3\u4e09\u5927\u521b\u65b0：ORB\u7279\u5f81\u63d0\u53d6+\u56db\u53c9\u6811\u5206\u5e03\u3001\u4e09\u7ebf\u7a0b\u5e76\u884c\u3001DBoW2\u56de\u73af+\u5168\u5c40BA\u3002ROS2\u96c6\u6210\u540e\u53ef\u5b9e\u73b0\u5b9e\u65f6\u89c6\u89c9\u5b9a\u4f4d\u5efa\u56fe\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

ORB-SLAM3\u89c6\u89c9SLAM\u539f\u7406\u4e0eROS2\u5b9e\u6218
\n\u4e00\u3001\u5f15\u8a00
\nSLAM(\u540c\u65f6\u5b9a\u4f4d\u4e0e\u5efa\u56fe)\u662f\u673a\u5668\u4eba\u548cAR\u7684\u6838\u5fc3\u3002ORB-SLAM3\u662f\u89c6\u89c9SLAM\u7684SOTA\u65b9\u6848，\u652f\u6301\u5355\u76ee\/\u53cc\u76ee\/RGB-DIMU\u878d\u5408，\u80fd\u5728\u52a8\u6001\u73af\u5883\u4e2d\u9c81\u68d2\u8fd0\u884c\u3002\u672c\u6587\u5c06\u89e3\u6790\u5176\u4e09\u5927\u7ebf\u7a0b\u5e76\u7ed9\u51faROS2\u96c6\u6210\u3002
\n\u4e8c\u3001\u4e09\u7ebf\u7a0b\u67b6\u6784
\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u5e27 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u5173\u952e\u5e27 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
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