lidar-visual-fusion

name	lidar-visual-fusion
description	激光视觉融合 SLAM 技能 - LIO-SAM、LVI-SAM、FusionSLAM、稠密重建
argument-hint	激光视觉融合 OR LIO-SAM OR LVI-SAM OR fusion slam
user-invocable	true

激光视觉融合 SLAM 技能

激光雷达与视觉融合的 SLAM

---

何时使用

当需要以下帮助时使用此技能：

• LIO-SAM 配置
• LVI-SAM 部署
• 激光视觉紧融合
• 稠密地图构建
• 闭环检测

---

核心实现

LIO-SAM 架构

# lio_sam.configure
lio_sam:
  # 点云配准
  pointCloudRegistration:
    scanContext:
      row: 20
      col: 60
      scanRadius: 50.0
      historySize: 10
      
  # IMU 预积分
  imuPreintegration:
    imuTopic: /imu
    deltaVDisablingThres: 0.001
    deltaQDisablingThres: 0.001
    gravity: -9.81
    
  # GPS 融合 (可选)
  gpsIntegration:
    gpsTopic: /gps/fix
    gpsAccThreshold: 2.0

ROS2 LIO-SAM 节点

import rclpy
from rclpy.node import Node
from sensor_msgs.msg import PointCloud2, Imu, NavSatFix
from nav_msgs.msg import Odometry
import numpy as np

class LIO-SAMNode(Node):
    def __init__(self):
        super().__init__('lio_sam')
        
        # 订阅
        self.point_cloud_sub = self.create_subscription(
            PointCloud2, '/lidar_points', self.cloud_callback, 10)
        self.imu_sub = self.create_subscription(
            Imu, '/imu', self.imu_callback, 10)
        self.gps_sub = self.create_subscription(
            NavSatFix, '/gps/fix', self.gps_callback, 10)
            
        # 发布
        self.odom_pub = self.create_publisher(Odometry, '/odom', 10)
        self.map_pub = self.create_publisher(PointCloud2, '/map', 10)
        
        # 初始化
        self.gps_handler = GPSHandler()
        self.imu_handler = IMUHandler()
        self.cloud_handler = CloudHandler()
        
     ():
        
        cloud = .cloud_handler.process(msg)
        
        
        imu_predicted = .imu_handler.predict()
        
        
        gps_correction = .gps_handler.get_correction()
        
        
        odometry = .optimize(cloud, imu_predicted, gps_correction)
        
        
        .publish_odom(odometry)

激光-视觉里程计融合

class LidarVisualOdometry:
    def __init__(self):
        self.lidar_odom = LaserOdometry()
        self.visual_odom = VisualOdometry()
        self.fusion = KalmanFilter()
        
    def compute_fused_odom(self, lidar_cloud, image):
        """融合激光和视觉里程计"""
        # 各自计算里程计
        lidar_pose = self.lidar_odom.compute_odometry(lidar_cloud)
        visual_pose = self.visual_odom.compute_pose(image)
        
        # 互相关估计置信度
        lidar_confidence = self.lidar_odom.get_confidence()
        visual_confidence = self.visual_odom.get_confidence()
        
        # 加权融合
        total = lidar_confidence + visual_confidence
        w_lidar = lidar_confidence / total
        w_visual = visual_confidence / total
        
        fused_pose = self.weighted_fusion(
            lidar_pose, visual_pose, w_lidar, w_visual)
            
        return fused_pose
        
    def weighted_fusion(self, pose1, pose2, w1, w2):
        """位姿加权融合"""
        # 位置直接加权
        p1 = pose1[:3, 3]
        p2 = pose2[:3, 3]
        fused_p = w1 * p1 + w2 * p2
        
        # 旋转使用 SLERP
        q1 = Rotation.from_matrix(pose1[:3, :3])
        q2 = Rotation.from_matrix(pose2[:3, :3])
        q_fused = q1.slerp(q2, w2)
        
        result = np.eye(4)
        result[:3, :] = q_fused.as_matrix()
        result[:, ] = fused_p
        
         result