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zL3Dy60;)&YR)YyJV?nhCb|^dkzrd(?gA0DfV#%Got!{h^ZA2$BZ2Iy+O{uBn-@Q9G zKTjyE@nj(^L$3}%(obK$yv3@<=5YF0lWdlRDZCh<6gd54)8CxtAUeO?oIqO{J+z9- z$r%P?Md$qA>y7H5mYVzVV>~nME5>adyF9n?9(YyU!Anybr~DL%6$Qww{eRDoOh$>f zLJ3Wyv}cMa+}kc5gCEzQs{rqPiZ=I|#N5$)+qVAsE{RmCR2;g|_tN6r*eMh>RPz!` zehQ5upfzWS97pfJAI!9Lk(BstpfLihotHvQE&R zhi(WtOF@BPE$t*97~|u|Uttx0^X2^S&vFVQ2=MYA!IZ#9PsGWJ{{>ePi?pSr#Cx{? m_o$y{@sIiMw@KAOYwI&ki1IY#eXk_Gt#(xFNU@S>$o~O^ey9`x literal 0 HcmV?d00001 diff --git a/docs/how-to-guides/integrating-autoware/tuning-parameters-and-performance/tuning-parameters/perception-tuning/index.md b/docs/how-to-guides/integrating-autoware/tuning-parameters-and-performance/tuning-parameters/perception-tuning/index.md index 7ffdf19659a..a3f6c4c5e30 100644 --- a/docs/how-to-guides/integrating-autoware/tuning-parameters-and-performance/tuning-parameters/perception-tuning/index.md +++ b/docs/how-to-guides/integrating-autoware/tuning-parameters-and-performance/tuning-parameters/perception-tuning/index.md @@ -45,11 +45,109 @@ file: ... ``` +Also, you need to update the roi_sync.param.yaml parameter file according to your camera number. +Firstly, +please refer to the roi_cluster_fusion documentation for more information about this package. +Then, you will update your camera offsets. +For example, +if you have four cameras for the perception detection pipeline, +and you haven't measured their timestamps, +you can set these camera offsets to "0" as the initial value. +Please be careful with the offset array size; it must be equal to your camera count. + +```diff +- input_offset_ms: [61.67, 111.67, 45.0, 28.33, 78.33, 95.0] # 6 cameras ++ input_offset_ms: [0.0, 0.0, 0.0, 0.0] # 4 cameras +``` + +If you have used different namespaces for your camera and ROI topics, +you will need to add the input topics for camera_info, +image_raw, +and rois messages in the [`tier4_perception_component.launch.xml`](https://github.com/autowarefoundation/autoware_launch/blob/main/autoware_launch/launch/components/tier4_perception_component.launch.xml) file. + +```diff +- ++ +- ++ +- ++ +``` + ### Tuning ground segmentation -!!! warning +The ground segmentation package removes the ground points from the input point cloud for the perception pipeline. +In our campus environment, there are a lot of high slopes and rough roads. +Therefore, this condition makes it difficult to accurately segment ground and non-ground points. + +For example, when we pass over speed bumps, +there are a lot of false positives (ghost points) that appear as non-ground points, +as shown in the image below. + +

+ ![default-ground-segmentation](images/ground-remover-ghost-points.png){ align=center } +
+ There are some false positive (ghost) +points on the high-slope roads with default configurations. +
+
+ +These ghost points affect the motion planner of Autoware, +causing the vehicle to stop even though there is no obstacle on the road during autonomous driving. +We will reduce the number of false positive non-ground points +by fine-tuning the ground segmentation in Autoware. + +There are three different ground segmentation algorithms included in Autoware: +`ray_ground_filter`, `scan_ground_filter`, and `ransac_ground_filter`. +The default method is the `scan_ground_filter`. +Please refer to the [`ground_segmentation` package documentation](https://autowarefoundation.github.io/autoware.universe/main/perception/ground_segmentation/) +for more information about these methods and their parameter definitions. + +Firstly, +we will change the `global_slope_max_angle_deg` value from 10 to 30 degrees at [`ground_segmentation.param.yaml`](https://github.com/autowarefoundation/autoware_launch/blob/main/autoware_launch/config/perception/obstacle_segmentation/ground_segmentation/ground_segmentation.param.yaml) parameter file. +This change will reduce our false positive non-ground points. +However, be cautious when increasing the threshold, +as it may lead to an increase in the number of false negatives. + +```diff +- global_slope_max_angle_deg: 10.0 ++ global_slope_max_angle_deg: 30.0 +``` + +Then we will update the split_height_distance parameter from 0.2 to 0.35 meters. +This adjustment will help in reducing false positive non-ground points, +especially on step-like road surfaces or in cases of misaligned multiple lidar configurations. + +```diff +- split_height_distance: 0.2 ++ split_height_distance: 0.35 +``` + +Now, we will change the non_ground_height_threshold value from 0.2 to 0.3 meters. +This will help us in reducing false positive non-ground points, +but it may also decrease the number of true positive non-ground points +that are below this threshold value. + +```diff +- non_ground_height_threshold: 0.2 ++ non_ground_height_threshold: 0.3 +``` + +The following image illustrates the results after these fine-tunings with the ground remover package. + +
+ ![after-ground-segmentation](images/after-tuning-ground-segmentation.png){ align=center } +
+ After tuning the ground segmentation, +the false positive points will disappear from the same location. +
+
- under construction +You need to update the ground segmenation according to your environment. +These examples are provided for high slopes and rough road conditions. +If you have better conditions, +you can adjust your parameters +by referring to the [`ground_segmentation` package documentation page](https://autowarefoundation.github.io/autoware.universe/main/perception/ground_segmentation/). ### Tuning euclidean clustering