[Raw Data] KiTTI Raw Dataset Format

[Durant35]

• image_0x/data/%010d.png、velodyne_points/data/%010d.bin以及oxts/data/%010d.txt已经通过时间戳完成同步

[Durant35]

追踪障碍物信息tracklet_labels.xml

<?xml version="1.0" encoding="UTF-8" standalone="yes" ?>
<!DOCTYPE boost_serialization>
<boost_serialization signature="serialization::archive" version="9">
<tracklets class_id="0" tracking_level="0" version="0">
    <count>15</count>
    <item_version>1</item_version>
    <item class_id="1" tracking_level="0" version="1">
        <objectType>Car</objectType>
        <h>1.4165441</h>
        <w>1.4749714</w>
        <l>3.5200996</l>
        <first_frame>0</first_frame>
        <poses class_id="2" tracking_level="0" version="0">
            <count>8</count>
            <item_version>2</item_version>
            <item class_id="3" tracking_level="0" version="2">
                <tx>12.085213857771446</tx>
                <ty>3.258426011375863</ty>
                <tz>-1.5901954174041748</tz>
                <rx>0</rx>
                <ry>0</ry>
                <rz>-3.9255512892016515</rz>
                <state>2</state>
                <occlusion>1</occlusion>
                <occlusion_kf>1</occlusion_kf>
                <truncation>0</truncation>
                <amt_occlusion>1</amt_occlusion>
                <amt_occlusion_kf>1</amt_occlusion_kf>
                <amt_border_l>0</amt_border_l>
                <amt_border_r>1</amt_border_r>
                <amt_border_kf>-1</amt_border_kf>
            </item>
            <item>
                <tx>11.387213028658699</tx>
                <ty>3.8984653734884813</ty>
                <tz>-1.557999849319458</tz>
                <rx>0</rx>
                <ry>0</ry>
                <rz>-3.8725313722361929</rz>
                <state>1</state>
                <occlusion>1</occlusion>
                <occlusion_kf>0</occlusion_kf>
                <truncation>0</truncation>
                <amt_occlusion>1</amt_occlusion>
                <amt_occlusion_kf>0</amt_occlusion_kf>
                <amt_border_l>0</amt_border_l>
                <amt_border_r>1</amt_border_r>
                <amt_border_kf>-1</amt_border_kf>
            </item>
            <item>
                <tx>10.599838591482998</tx>
                <ty>4.5330548134433801</ty>
                <tz>-1.5711039304733276</tz>
                <rx>0</rx>
                <ry>0</ry>
                <rz>-3.8135162367331068</rz>
                <state>1</state>
                <occlusion>1</occlusion>
                <occlusion_kf>0</occlusion_kf>
                <truncation>0</truncation>
                <amt_occlusion>1</amt_occlusion>
                <amt_occlusion_kf>1</amt_occlusion_kf>
                <amt_border_l>0</amt_border_l>
                <amt_border_r>1</amt_border_r>
                <amt_border_kf>-1</amt_border_kf>
            </item>

• 轨迹总数：15，每条轨迹是连续的

  <tracklets class_id="0" tracking_level="0" version="0">
  <count>15</count>

• 单条轨迹上障碍物信息总览：障碍物类别 Car、OBB 长宽高、障碍物出现在0-7连续8帧点云中

  <item class_id="1" tracking_level="0" version="1">
  <objectType>Car</objectType>
  <h>1.4165441</h>
  <w>1.4749714</w>
  <l>3.5200996</l>
  <first_frame>0</first_frame>
      <poses class_id="2" tracking_level="0" version="0">
      <count>8</count>
      <item_version>2</item_version>

• 每帧点云中障碍物出现的位置（相对于当前LiDAR位置）：平移(tx, ty, tz)+旋转(rx, ry, rz)

  <item class_id="3" tracking_level="0" version="2">
  <tx>12.085213857771446</tx>
  <ty>3.258426011375863</ty>
  <tz>-1.5901954174041748</tz>
  <rx>0</rx>
  <ry>0</ry>
  <rz>-3.9255512892016515</rz>
  <state>2</state>
  <occlusion>1</occlusion>
  <occlusion_kf>1</occlusion_kf>
  <truncation>0</truncation>
  <amt_occlusion>1</amt_occlusion>
  <amt_occlusion_kf>1</amt_occlusion_kf>
  <amt_border_l>0</amt_border_l>
  <amt_border_r>1</amt_border_r>
  <amt_border_kf>-1</amt_border_kf>
  </item>