From d4b64d317ee6bc969762ba7d0a56b6e73d404eab Mon Sep 17 00:00:00 2001 From: Kazuki Miyahara Date: Mon, 22 Mar 2021 21:43:13 +0900 Subject: [PATCH] Fix typos in docs (#186) --- docs/Credits.md | 6 +++--- docs/porting-to-ROS2.md | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/Credits.md b/docs/Credits.md index 2eaff262ea735..a9312d86f3b2a 100644 --- a/docs/Credits.md +++ b/docs/Credits.md @@ -1,10 +1,10 @@ -Some pre-trained models provided by other repository are used in some packages. +Some pre-trained models provided by other repositories are used in some packages. - tensorrt_yolo3
The pre-trained models are provided in the following repository. The trained file is automatically downloaded when you build.
https://github.com/lewes6369/TensorRT-Yolov3
\[Original URL]
-Tranined file (416) : https://drive.google.com/drive/folders/18OxNcRrDrCUmoAMgngJlhEglQ1Hqk_NJ +Trained file (416) : https://drive.google.com/drive/folders/18OxNcRrDrCUmoAMgngJlhEglQ1Hqk_NJ - traffic_light_fine_detector
A trained model in this package is based on the following .weights file and was fine-tuned with darknet by Tier IV.
@@ -14,7 +14,7 @@ After fine-tuning, the trained model is converted to ONNX file with the followin https://github.com/tier4/Pilot.Auto/blob/master/src/perception/traffic_light_recognition/traffic_light_fine_detector/scripts/yolov3_to_onnx.py
- lidar_apollo_instance_segmentation
-This package makes use of three pre-trained models provided by apollo. These files are automatically downloaded when you build.
+This package makes use of three pre-trained models provided by Apollo. These files are automatically downloaded when you build.
\[Original URL]
VLP-16 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne16/deploy.caffemodel
HDL-64 : https://github.com/ApolloAuto/apollo/raw/88bfa5a1acbd20092963d6057f3a922f3939a183/modules/perception/production/data/perception/lidar/models/cnnseg/velodyne64/deploy.caffemodel
diff --git a/docs/porting-to-ROS2.md b/docs/porting-to-ROS2.md index a361417ec9353..1129fd7756340 100644 --- a/docs/porting-to-ROS2.md +++ b/docs/porting-to-ROS2.md @@ -151,7 +151,7 @@ In ROS2, you should define semantically meaningful wrappers around primitive (nu ### Changing the namespaces and header files for generated message types -If you follow the migration guide and change the included headers to have an extra `/msg` in the path and convert to `snake_case`, you might get a cryptic error. Turns out _two_ files are being generated: One for C types (`.h` headers) and and one for CPP types (`.hpp` headers). So don't forget to change `.h` to `.hpp` too. Also, don't forget to insert an additional `::msg` between the package namespace and the class name. +If you follow the migration guide and change the included headers to have an extra `/msg` in the path and convert to `snake_case`, you might get a cryptic error. Turns out _two_ files are being generated: One for C types (`.h` headers) and one for CPP types (`.hpp` headers). So don't forget to change `.h` to `.hpp` too. Also, don't forget to insert an additional `::msg` between the package namespace and the class name. A tip: Sublime Text has a handy "Case Conversion" package for converting to snake case. @@ -325,7 +325,7 @@ DECLARE_MPC_PARAM(mpc_param_, prediction_horizon, 50); set_param_res_ = add_on_set_parameters_callback(std::bind(&MPCFollower::paramCallback, this, _1)); ``` -Inside the callback, you have to manually update each parameter for which you want to react to changes from the outside. You can (inadvertently) declare more parameters than you react to. +Inside the callback, you have to manually update each parameter for which you want to react to change from the outside. You can (inadvertently) declare more parameters than you react to. ```c++ rcl_interfaces::msg::SetParametersResult result;