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latest/contributing/coding-guidelines/ros-nodes/parameters/index.html

@@ -7869,7 +7869,7 @@ <h2 id="json-schema">JSON Schema<a class="headerlink" href="#json-schema" title=
 <span class="p">}</span>
 </code></pre></div>
 <p>The schema file path is <code>INSERT_PATH_TO_PACKAGE/schema/</code> and the schema file name is <code>INSERT_NODE_NAME.schema.json</code>. To adapt the template to the <abbr title="Robot Operating System">ROS</abbr> node, replace each <code>INSERT_...</code> and add all parameters <code>1..N</code>.</p>
-<p>See example: <em>Lidar Apollo Segmentation TVM Nodes</em> <a href="https://github.com/autowarefoundation/autoware.universe/blob/main/perception/lidar_apollo_segmentation_tvm_nodes/schema/lidar_apollo_segmentation_tvm_nodes.schema.json">schema</a></p>
+<p>See example: <em>Image Projection Based Fusion - Pointpainting</em> <a href="https://github.com/autowarefoundation/autoware.universe/blob/main/universe/perception/autoware_image_projection_based_fusion/schema/pointpainting.schema.json">schema</a></p>
 <h3 id="attributes">Attributes<a class="headerlink" href="#attributes" title="Permanent link">#</a></h3>
 <p>Parameters have several attributes, some are required and some optional. The optional attributes are highly encouraged when applicable, as they provide useful information about a parameter and can ensure the value of the parameter is within its bounds.</p>
 <h4 id="required">Required<a class="headerlink" href="#required" title="Permanent link">#</a></h4>

latest/design/autoware-architecture/perception/reference-implementations/radar-based-3d-detector/faraway-object-detection/index.html

@@ -7775,37 +7775,37 @@ <h2 id="overview">Overview<a class="headerlink" href="#overview" title="Permanen
 <h2 id="reference-implementation">Reference implementation<a class="headerlink" href="#reference-implementation" title="Permanent link">#</a></h2>
 <h3 id="crossing-filter">Crossing filter<a class="headerlink" href="#crossing-filter" title="Permanent link">#</a></h3>
 <ul>
-<li><a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/radar_crossing_objects_noise_filter">radar_crossing_objects_noise_filter</a></li>
+<li><a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/autoware_radar_crossing_objects_noise_filter">radar_crossing_objects_noise_filter</a></li>
 </ul>
 <p>This package can filter the noise objects crossing to the ego vehicle, which are most likely ghost objects.</p>
 <h3 id="velocity-filter">Velocity filter<a class="headerlink" href="#velocity-filter" title="Permanent link">#</a></h3>
 <ul>
-<li><a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/object_velocity_splitter">object_velocity_splitter</a></li>
+<li><a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/autoware_object_velocity_splitter">object_velocity_splitter</a></li>
 </ul>
 <p>Static objects include many noise like the objects reflected from ground.
 In many cases for radars, dynamic objects can be detected stably.
 To filter out static objects, <code>object_velocity_splitter</code> can be used.</p>
 <h3 id="range-filter">Range filter<a class="headerlink" href="#range-filter" title="Permanent link">#</a></h3>
 <ul>
-<li><a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/object_range_splitter">object_range_splitter</a></li>
+<li><a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/autoware_object_range_splitter">object_range_splitter</a></li>
 </ul>
 <p>For some radars, ghost objects sometimes occur for near objects.
 To filter these objects, <code>object_range_splitter</code> can be used.</p>
 <h3 id="vector-map-filter">Vector map filter<a class="headerlink" href="#vector-map-filter" title="Permanent link">#</a></h3>
 <ul>
-<li><a href="https://github.com/autowarefoundation/autoware.universe/blob/main/perception/detected_object_validation/object-lanelet-filter.md">object-lanelet-filter</a></li>
+<li><a href="https://github.com/autowarefoundation/autoware.universe/blob/main/perception/autoware_detected_object_validation/object-lanelet-filter.md">object-lanelet-filter</a></li>
 </ul>
 <p>In most cases, vehicles drive in drivable are.
 To filter objects that are out of drivable area, <code>object-lanelet-filter</code> can be used.
 <code>object-lanelet-filter</code> filter objects that are out of drivable area defined by vector map.</p>
 <p>Note that if you use <code>object-lanelet-filter</code> for radar faraway detection, you need to define drivable area in a vector map other than the area where autonomous car run.</p>
 <h3 id="radar-object-clustering">Radar object clustering<a class="headerlink" href="#radar-object-clustering" title="Permanent link">#</a></h3>
 <ul>
-<li><a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/radar_object_clustering">radar_object_clustering</a></li>
+<li><a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/autoware_radar_object_clustering">radar_object_clustering</a></li>
 </ul>
 <p>This package can combine multiple radar detections from one object into one and adjust class and size.
 It can suppress splitting objects in tracking module.</p>
-<p><img alt="radar_object_clustering" src="https://raw.githubusercontent.com/autowarefoundation/autoware.universe/main/perception/radar_object_clustering/docs/radar_clustering.drawio.svg"></p>
+<p><img alt="radar_object_clustering" src="https://raw.githubusercontent.com/autowarefoundation/autoware.universe/main/perception/autoware_radar_object_clustering/docs/radar_clustering.drawio.svg"></p>
 <h2 id="note">Note<a class="headerlink" href="#note" title="Permanent link">#</a></h2>
 <h3 id="parameter-tuning">Parameter tuning<a class="headerlink" href="#parameter-tuning" title="Permanent link">#</a></h3>
 <p>Detection performed only by Radar applies various strong noise processing.

latest/design/autoware-architecture/perception/reference-implementations/radar-based-3d-detector/radar-based-3d-detector/index.html

@@ -7863,7 +7863,7 @@ <h3 id="noise-filter-and-radar-faraway-dynamic-3d-object-detection">Noise filter
 In detail, please see <a href="../faraway-object-detection/">this document</a></p>
 <h3 id="radar-fusion-to-lidar-based-3d-object-detection">Radar fusion to LiDAR-based 3D object detection<a class="headerlink" href="#radar-fusion-to-lidar-based-3d-object-detection" title="Permanent link">#</a></h3>
 <ul>
-<li><a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/radar_fusion_to_detected_object">radar_fusion_to_detected_object</a></li>
+<li><a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/autoware_radar_fusion_to_detected_object">radar_fusion_to_detected_object</a></li>
 </ul>
 <p>This package contains a sensor fusion module for radar-detected objects and 3D detected objects. The fusion node can:</p>
 <ul>

latest/design/autoware-architecture/sensing/data-types/radar-data/radar-objects-data/index.html

@@ -7845,19 +7845,19 @@ <h3 id="noise-filter">Noise filter<a class="headerlink" href="#noise-filter" tit
 <p>Radar can detect x-axis velocity as doppler velocity, but cannot detect y-axis velocity. Some radar can estimate y-axis velocity inside the device, but it sometimes lack precision. This package treats these objects as noise by y-axis threshold filter.</p>
 <h3 id="message-converter">Message converter<a class="headerlink" href="#message-converter" title="Permanent link">#</a></h3>
 <ul>
-<li><a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/radar_tracks_msgs_converter">radar_tracks_msgs_converter</a></li>
+<li><a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/autoware_radar_tracks_msgs_converter">radar_tracks_msgs_converter</a></li>
 </ul>
 <p>This package converts from <code>radar_msgs/msg/RadarTracks</code> into <code>autoware_auto_perception_msgs/msg/DetectedObject</code> with ego vehicle motion compensation and coordinate transform.</p>
 <h3 id="object-merger">Object merger<a class="headerlink" href="#object-merger" title="Permanent link">#</a></h3>
 <ul>
-<li><a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/object_merger">object_merger</a></li>
+<li><a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/autoware_object_merger">object_merger</a></li>
 </ul>
 <p>This package can merge 2 topics of <code>autoware_auto_perception_msgs/msg/DetectedObject</code>.</p>
 <ul>
-<li><a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/simple_object_merger">simple_object_merger</a></li>
+<li><a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/autoware_simple_object_merger">simple_object_merger</a></li>
 </ul>
 <p>This package can merge simply multiple topics of <code>autoware_auto_perception_msgs/msg/DetectedObject</code>.
-Different from <a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/object_merger">object_merger</a>, this package doesn't use association algorithm and can merge with low calculation cost.</p>
+Different from <a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/autoware_object_merger">object_merger</a>, this package doesn't use association algorithm and can merge with low calculation cost.</p>
 <ul>
 <li><a href="https://github.com/ros-tooling/topic_tools">topic_tools</a></li>
 </ul>

latest/design/autoware-architecture/sensing/data-types/radar-data/radar-pointcloud-data/index.html

@@ -7884,7 +7884,7 @@ <h3 id="message-converter-from-radarscan-to-pointcloud2">Message converter from
 <p>For considered use cases,</p>
 <ul>
 <li>Use <a href="https://github.com/autowarefoundation/autoware.universe/tree/main/sensing/pointcloud_preprocessor">pointcloud_preprocessor</a> for radar scan.</li>
-<li>Apply obstacle segmentation like <a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/ground_segmentation">ground segmentation</a> to radar points for LiDAR-less (camera + radar) systems.</li>
+<li>Apply obstacle segmentation like <a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/autoware_ground_segmentation">ground segmentation</a> to radar points for LiDAR-less (camera + radar) systems.</li>
 </ul>
 <h2 id="appendix">Appendix<a class="headerlink" href="#appendix" title="Permanent link">#</a></h2>
 <h3 id="discussion">Discussion<a class="headerlink" href="#discussion" title="Permanent link">#</a></h3>

latest/design/autoware-architecture/sensing/data-types/radar-data/reference-implementations/data-message/index.html

@@ -7819,7 +7819,7 @@ <h3 id="message-usage-for-radartracks">Message usage for RadarTracks<a class="he
 uint16<span class="w"> </span><span class="nv">BICYCLE</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">32006</span><span class="p">;</span>
 uint16<span class="w"> </span><span class="nv">PEDESTRIAN</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">32007</span><span class="p">;</span>
 </code></pre></div>
-<p>For detail implementation, please see <a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/radar_tracks_msgs_converter">radar_tracks_msgs_converter</a>.</p>
+<p>For detail implementation, please see <a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/autoware_radar_tracks_msgs_converter">radar_tracks_msgs_converter</a>.</p>
 <h2 id="note">Note<a class="headerlink" href="#note" title="Permanent link">#</a></h2>
 <h3 id="survey-for-radar-message">Survey for radar message<a class="headerlink" href="#survey-for-radar-message" title="Permanent link">#</a></h3>
 <p>Depending on the sensor manufacturer and its purpose, each sensor might exchange raw, post-processed data. This section introduces a survey about the previously developed messaging systems in the open-source community. Although there are many kinds of outputs, radar mainly adopt two types as outputs, pointcloud and objects. Related discussion for message definition in ros-perception are <a href="https://github.com/ros-perception/radar_msgs/pull/1">PR #1</a>, <a href="https://github.com/ros-perception/radar_msgs/pull/2">PR #2</a>, and <a href="https://github.com/ros-perception/radar_msgs/pull/3">PR #3</a>. Existing open source softwares for radar are summarized in these PR.</p>

latest/how-to-guides/integrating-autoware/creating-vehicle-and-sensor-model/creating-sensor-model/index.html

@@ -8350,7 +8350,7 @@ <h3 id="camera-launching">Camera Launching<a class="headerlink" href="#camera-la
   for example, we will use <code>/perception/object_detection</code> as tensorrt_yolo node namespace,
   it will be explained in autoware usage section.
   For more information,
-  please check <a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/image_projection_based_fusion">image_projection_based_fusion</a> package.</li>
+  please check <a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/autoware_image_projection_based_fusion">image_projection_based_fusion</a> package.</li>
 </ul>
 <p>After the preparing <code>camera_node_container.launch.py</code> to our forked <code>common_sensor_launch</code> package,
 we need to build the package:</p>

latest/how-to-guides/integrating-autoware/launch-autoware/index.html

@@ -7780,7 +7780,7 @@ <h3 id="perception">Perception<a class="headerlink" href="#perception" title="Pe
 <p>If you want to use traffic light recognition and visualization,
 you can set <code>traffic_light_recognition/enable_fine_detection</code> as true (default).
 Please check
-<a href="https://autowarefoundation.github.io/autoware.universe/main/perception/traffic_light_fine_detector/">traffic_light_fine_detector</a>
+<a href="https://autowarefoundation.github.io/autoware.universe/main/perception/autoware_traffic_light_fine_detector/">traffic_light_fine_detector</a>
 page for more information.
 If you don't want to use traffic light classifier, then you can disable it:</p>
 <div class="highlight"><pre><span></span><code><span class="gd">- &lt;arg name="traffic_light_recognition/enable_fine_detection" default="true" description="enable traffic light fine detection"/&gt;</span>

latest/how-to-guides/integrating-autoware/launch-autoware/perception/index.html

@@ -7627,7 +7627,7 @@ <h2 id="tier4_perception_componentlaunchxml">tier4_perception_component.launch.x
 Here are some predefined perception launch arguments:</p>
 <ul>
 <li>
-<p><strong><code>occupancy_grid_map_method:</code></strong> This argument determines the occupancy grid map method for perception stack. Please check <a href="https://autowarefoundation.github.io/autoware.universe/main/perception/probabilistic_occupancy_grid_map/">probabilistic_occupancy_grid_map</a> package for detailed information.
+<p><strong><code>occupancy_grid_map_method:</code></strong> This argument determines the occupancy grid map method for perception stack. Please check <a href="https://autowarefoundation.github.io/autoware.universe/main/perception/autoware_probabilistic_occupancy_grid_map/">probabilistic_occupancy_grid_map</a> package for detailed information.
   The default probabilistic occupancy grid map method is <code>pointcloud_based_occupancy_grid_map</code>.
   If you want to change it to the <code>laserscan_based_occupancy_grid_map</code>, you can change it here:</p>
 <div class="highlight"><pre><span></span><code><span class="gd">- &lt;arg name="occupancy_grid_map_method" default="pointcloud_based_occupancy_grid_map" description="options: pointcloud_based_occupancy_grid_map, laserscan_based_occupancy_grid_map"/&gt;</span>
@@ -7638,7 +7638,7 @@ <h2 id="tier4_perception_componentlaunchxml">tier4_perception_component.launch.x
 <ul>
 <li>
 <p><strong><code>detected_objects_filter_method:</code></strong> This argument determines the filter method for detected objects.
-  Please check <a href="https://autowarefoundation.github.io/autoware.universe/main/perception/detected_object_validation/">detected_object_validation</a> package for detailed information about lanelet and position filter.
+  Please check <a href="https://autowarefoundation.github.io/autoware.universe/main/perception/autoware_detected_object_validation/">detected_object_validation</a> package for detailed information about lanelet and position filter.
   The default detected object filter method is <code>lanelet_filter</code>.
   If you want to change it to the <code>position_filter</code>, you can change it here:</p>
 <div class="highlight"><pre><span></span><code><span class="gd">- &lt;arg name="detected_objects_filter_method" default="lanelet_filter" description="options: lanelet_filter, position_filter"/&gt;</span>
@@ -7649,7 +7649,7 @@ <h2 id="tier4_perception_componentlaunchxml">tier4_perception_component.launch.x
 <ul>
 <li>
 <p><strong><code>detected_objects_validation_method:</code></strong> This argument determines the validation method for detected objects.
-  Please check <a href="https://autowarefoundation.github.io/autoware.universe/main/perception/detected_object_validation/">detected_object_validation</a> package for detailed information about validation methods.
+  Please check <a href="https://autowarefoundation.github.io/autoware.universe/main/perception/autoware_detected_object_validation/">detected_object_validation</a> package for detailed information about validation methods.
   The default detected object filter method is <code>obstacle_pointcloud</code>.
   If you want to change it to the <code>occupancy_grid</code>, you can change it here,
   but remember it requires <code>laserscan_based_occupancy_grid_map</code> method as <code>occupancy_grid_map_method</code>:</p>
@@ -7687,7 +7687,7 @@ <h2 id="perceptionlaunchxml">perception.launch.xml<a class="headerlink" href="#p
 <ul>
 <li>
 <p><strong><code>remove_unknown:</code></strong> This parameter determines the remove unknown objects at camera-lidar fusion.
-  Please check <a href="https://github.com/autowarefoundation/autoware.universe/blob/main/perception/image_projection_based_fusion/docs/roi-cluster-fusion.md">roi_cluster_fusion</a> node for detailed information.
+  Please check <a href="https://github.com/autowarefoundation/autoware.universe/blob/main/perception/autoware_image_projection_based_fusion/docs/roi-cluster-fusion.md">roi_cluster_fusion</a> node for detailed information.
   The default value is <code>true</code>.
   If you want to change it to the <code>false</code>,
   you can add this argument to <code>tier4_perception_component.launch.xml</code>,

latest/how-to-guides/others/running-autoware-without-cuda/index.html

@@ -7644,7 +7644,7 @@ <h2 id="running-2d3d-object-detection-without-cuda">Running 2D/3D object detecti
 <li><code>lidar-centerpoint</code> + <code>tensorrt_yolo</code></li>
 <li><code>euclidean_cluster</code></li>
 </ul>
-<p>Of these five configurations, only the last one (<code>euclidean_cluster</code>) can be run without CUDA. For more details, refer to the <a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/euclidean_cluster"><code>euclidean_cluster</code> module's README file</a>.</p>
+<p>Of these five configurations, only the last one (<code>euclidean_cluster</code>) can be run without CUDA. For more details, refer to the <a href="https://github.com/autowarefoundation/autoware.universe/tree/main/perception/autoware_euclidean_cluster"><code>euclidean_cluster</code> module's README file</a>.</p>
 <h2 id="running-traffic-light-detection-without-cuda">Running traffic light detection without CUDA<a class="headerlink" href="#running-traffic-light-detection-without-cuda" title="Permanent link">#</a></h2>
 <p>For traffic light recognition (both detection and classification), there are two modules that require CUDA:</p>
 <ul>