LeGO-LOAM分析之配置（四）

梦里梦外; 2022-09-12 05:42 238阅读 0赞

转载地址：[https://zhuanlan.zhihu.com/p/386449627][https_zhuanlan.zhihu.com_p_386449627]  
这一章我们主要看下LeGO-LOAM的配置文件，有哪些参数，以及这些参数的作用是什么？

### 激光雷达配置 ###

激光雷达原始点云topic和imu的输入topic

extern const string pointCloudTopic = "/velodyne_points";
    extern const string imuTopic = "/imu/data";

点云保存目录，以及是否采用ring（velodyne支持，其它目前不清楚）

// Save pcd
    extern const string fileDirectory = "/tmp/";
    
    // Using velodyne cloud "ring" channel for image projection (other lidar may have different name for this channel, change "PointXYZIR" below)
    extern const bool useCloudRing = true; // if true, ang_res_y and ang_bottom are not used

支持的lidar，论文中使用的是velodyne 16线雷达，如果是其它lidar需要适配这些参数。

// VLP-16
    extern const int N_SCAN = 16;     //  线数
    extern const int Horizon_SCAN = 1800;  // 旋转一周采样次数
    extern const float ang_res_x = 0.2;     // 水平分辨率
    extern const float ang_res_y = 2.0;     // 垂直分辨率
    extern const float ang_bottom = 15.0+0.1;   // lidar 底部线束的角度偏移
    extern const int groundScanInd = 7;         // lidar 判定为地面的线数
    // HDL-32E
    // extern const int N_SCAN = 32;
    // extern const int Horizon_SCAN = 1800;
    // extern const float ang_res_x = 360.0/float(Horizon_SCAN);
    // extern const float ang_res_y = 41.33/float(N_SCAN-1);
    // extern const float ang_bottom = 30.67;
    // extern const int groundScanInd = 20;
    
    // VLS-128
    // extern const int N_SCAN = 128;
    // extern const int Horizon_SCAN = 1800;
    // extern const float ang_res_x = 0.2;
    // extern const float ang_res_y = 0.3;
    // extern const float ang_bottom = 25.0;
    // extern const int groundScanInd = 10;
    
    // Ouster users may need to uncomment line 159 in imageProjection.cpp
    // Usage of Ouster imu data is not fully supported yet (LeGO-LOAM needs 9-DOF IMU), please just publish point cloud data
    // Ouster OS1-16
    // extern const int N_SCAN = 16;
    // extern const int Horizon_SCAN = 1024;
    // extern const float ang_res_x = 360.0/float(Horizon_SCAN);
    // extern const float ang_res_y = 33.2/float(N_SCAN-1);
    // extern const float ang_bottom = 16.6+0.1;
    // extern const int groundScanInd = 7;
    
    // Ouster OS1-64
    // extern const int N_SCAN = 64;
    // extern const int Horizon_SCAN = 1024;
    // extern const float ang_res_x = 360.0/float(Horizon_SCAN);
    // extern const float ang_res_y = 33.2/float(N_SCAN-1);
    // extern const float ang_bottom = 16.6+0.1;
    // extern const int groundScanInd = 15;

接下来我们在详细分析下算法相关的参数。

// 是否需要回环检测
    extern const bool loopClosureEnableFlag = false;
    // 建图时间间隔（周期）
    extern const double mappingProcessInterval = 0.3;
    // imu扫描频率
    extern const float scanPeriod = 0.1;
    // 系统延迟
    extern const int systemDelay = 0;
    // imu缓存大小
    extern const int imuQueLength = 200;
    // 过滤小于1m之内的点云
    extern const float sensorMinimumRange = 1.0;
    // ?
    extern const float sensorMountAngle = 0.0;
    // 点云分割每个方向的角度大小
    extern const float segmentTheta = 60.0/180.0*M_PI; // decrese this value may improve accuracy
    // 点云分割有效点和有效线的大小
    extern const int segmentValidPointNum = 5;
    extern const int segmentValidLineNum = 3;
    // 水平方向最小分辨率弧度
    extern const float segmentAlphaX = ang_res_x / 180.0 * M_PI;
    // 垂直方向最小分辨率弧度
    extern const float segmentAlphaY = ang_res_y / 180.0 * M_PI;
    // 线特征数量
    extern const int edgeFeatureNum = 2;
    // 面特征数量
    extern const int surfFeatureNum = 4;
    // 没有使用到的参数
    extern const int sectionsTotal = 6;
    // 曲率阈值
    extern const float edgeThreshold = 0.1;
    extern const float surfThreshold = 0.1;
    // 特征查找半径
    extern const float nearestFeatureSearchSqDist = 25;
    
    // Mapping Params
    // 点云地图搜索半径
    extern const float surroundingKeyframeSearchRadius = 50.0; // key frame that is within n meters from current pose will be considerd for scan-to-map optimization (when loop closure disabled)
    // 点云地图搜索大小
    extern const int   surroundingKeyframeSearchNum = 50; // submap size (when loop closure enabled)
    // history key frames (history submap for loop closure)
    // 回环检测首尾距离
    extern const float historyKeyframeSearchRadius = 7.0; // key frame that is within n meters from current pose will be considerd for loop closure
    // 回环点云查找范围[-25, 25]
    extern const int   historyKeyframeSearchNum = 25; // 2n+1 number of hostory key frames will be fused into a submap for loop closure
    // icp匹配分数，平均距离小于0.3
    extern const float historyKeyframeFitnessScore = 0.3; // the smaller the better alignment
    // 可视化点云范围
    extern const float globalMapVisualizationSearchRadius = 500.0; // key frames with in n meters will be visualized

### 注册新的点云类型 ###

注册新的点云格式到pointcloud，这样就可以使用"pcl::PointCloud"而不会提示类型找不到了。

/*
        * A point cloud type that has "ring" channel
        */
    struct PointXYZIR
    {
        
        PCL_ADD_POINT4D
        PCL_ADD_INTENSITY;
        uint16_t ring;
        EIGEN_MAKE_ALIGNED_OPERATOR_NEW
    } EIGEN_ALIGN16;
    
    POINT_CLOUD_REGISTER_POINT_STRUCT (PointXYZIR,  
                                       (float, x, x) (float, y, y)
                                       (float, z, z) (float, intensity, intensity)
                                       (uint16_t, ring, ring)
    )
    
    /*
        * A point cloud type that has 6D pose info ([x,y,z,roll,pitch,yaw] intensity is time stamp)
        */
    struct PointXYZIRPYT
    {
        
        PCL_ADD_POINT4D
        PCL_ADD_INTENSITY;
        float roll;
        float pitch;
        float yaw;
        double time;
        EIGEN_MAKE_ALIGNED_OPERATOR_NEW
    } EIGEN_ALIGN16;
    
    POINT_CLOUD_REGISTER_POINT_STRUCT (PointXYZIRPYT,
                                       (float, x, x) (float, y, y)
                                       (float, z, z) (float, intensity, intensity)
                                       (float, roll, roll) (float, pitch, pitch) (float, yaw, yaw)
                                       (double, time, time)
    )
    
    typedef PointXYZIRPYT  PointTypePose;

### 总结 ###

LeGO-loam主要是要关注lidar的参数，以及换成其它品牌lidar之后的适配。

[https_zhuanlan.zhihu.com_p_386449627]: https://zhuanlan.zhihu.com/p/386449627