Robot: Code clean. (#7655)

modules/canbus/README.md

@@ -17,15 +17,15 @@ The major components in canbus module are:
 
   * **CAN Client** - CAN client has been moved to `/modules/drivers/canbus` since it is shared by different sensors utilizing the canbus protocol
 
-You can implement your own CAN client in the folder `can_client` by inheriting from the `CanClient` class. 
-  
+You can implement your own CAN client in the folder `can_client` by inheriting from the `CanClient` class.
+
 ```
 Note:
 Do not forget to register your CAN client in `CanClientFactory`.
 ```
 
-You can also implement your own vehicle controller and message manager in the folder `vehicle` by inheriting from `VehicleController` and `MessageManager`. 
-  
+You can also implement your own vehicle controller and message manager in the folder `vehicle` by inheriting from `VehicleController` and `MessageManager`.
+
 ```
 Note:
 Do not forget to register your vehicle in `VehicleFactory`.

modules/dreamview/README.md

@@ -2,7 +2,7 @@
 # Dreamview
 
 ## Introduction
-Dreamview or Apollo's HMI module provides a web application that helps developers visualize the output of other relevant autonomous driving modules, e.g. the vehicle's planning trajectory, car localization, chassis status etc. 
+Dreamview or Apollo's HMI module provides a web application that helps developers visualize the output of other relevant autonomous driving modules, e.g. the vehicle's planning trajectory, car localization, chassis status etc.
 
 ## Input
   Currently Dreamview monitors the following messages:
@@ -13,7 +13,7 @@ Dreamview or Apollo's HMI module provides a web application that helps developer
   * Perception Obstacles, defined by Protobuf message `PerceptionObstacles`, which can be found in file `perception/proto/perception_obstacle.proto`.
   * Prediction, defined by Protobuf message `PredictionObstacles`, which can be found in file `prediction/proto/prediction_obstacle.proto`.
   * Routing, defined by Protobuf message `RoutingResponse`, which can be found in file `routing/proto/routing.proto`.
-  
+
 ## Output
   A web-based dynamic 3D rendering of the monitored messages in a simulated world.
 

modules/localization/README.md

@@ -9,7 +9,7 @@
   In the provided RTK localization method, there are two inputs:
   - GPS - The Global Positioning System
   - IMU - Inertial Measurement Unit
-  
+
   In the  multi-sensor fusion localization method, there are three inputs:
   - GPS - The Global Positioning System
   - IMU - Inertial Measurement Unit
@@ -20,7 +20,7 @@
   Guowei Wan, Xiaolong Yang, Renlan Cai, Hao Li, Yao Zhou, Hao Wang, Shiyu Song. "Robust and Precise Vehicle Localization Based on Multi-Sensor Fusion in Diverse City Scenes," 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, 2018, pp. 4670-4677.
   doi: 10.1109/ICRA.2018.8461224. [link](https://ieeexplore.ieee.org/document/8461224)
 
-## Output 
+## Output
 An object instance defined by Protobuf message `LocalizationEstimate`, which can be found in file `localization/proto/localization.proto`.
 
 ## Implementing Localization
@@ -35,7 +35,7 @@ An object instance defined by Protobuf message `LocalizationEstimate`, which can
   ```
    localization_factory_.Register(LocalizationConfig::FOO, []()->LocalizationBase* { return new FooLocalization(); });
   ```
-  
+
   4. Make sure your code compiles by including the header files that defines class `FooLocalization`
 
   1. Now you can go back to the `apollo` root directory and build your code with command `bash apollo.sh build`

modules/localization/README_cn.md

@@ -28,7 +28,7 @@
 1. 在`proto/localization_config.proto`的`LocalizationType enum type`中添加Foo。
 
 1. 转到`modules/localization`目录，并创建一个Foo目录。在Foo目录中，根据rtk目录中的`RTKLocalization`类增加`FooLocalization`类。`FooLocalization`必须是`LocalizationBase`的子类。根据`rtk/BUILD`还需创建文件`foo/BUILD`。
- 
+
 1. 您需要在函数`Localization::RegisterLocalizationMethods()`中注册`FooLocalization`，它位于CPP文件`localization.cc`中。您可以通过在函数的末尾插入以下代码来注册：
 ```
 localization_factory_.Register(LocalizationConfig::FOO, []()->LocalizationBase* { return new FooLocalization(); });

modules/localization/msf/README.md

@@ -4,7 +4,7 @@
   The goal of multi-sensor localization is to provide a robust method which can achieve high localization accuracy and resilience in challenging scenes, such as urban downtown, highways, and tunnels. It adaptively uses information from complementary sensors such as GNSS, LiDAR and IMU. If you want to know more details, please refer to our paper
   *Guowei Wan, Xiaolong Yang, Renlan Cai, Hao Li, Yao Zhou, Hao Wang, Shiyu Song. "Robust and Precise Vehicle Localization Based on Multi-Sensor Fusion in Diverse City Scenes," 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, 2018, pp. 4670-4677.
   doi: 10.1109/ICRA.2018.8461224.* [link](https://ieeexplore.ieee.org/document/8461224)
-  
+
 
   Currently this localization method only works for x86_64 platform
 

modules/localization/msf/common/util/frame_transform.cc

@@ -40,7 +40,7 @@ bool FrameTransform::LatlonToUtmXY(double lon_rad, double lat_rad,
   }
   double longitude = lon_rad;
   double latitude = lat_rad;
-  pj_transform(pj_latlon, pj_utm, 1, 1, &longitude, &latitude, NULL);
+  pj_transform(pj_latlon, pj_utm, 1, 1, &longitude, &latitude, nullptr);
   utm_xy->x = longitude;
   utm_xy->y = latitude;
   pj_free(pj_latlon);
@@ -61,7 +61,7 @@ bool FrameTransform::UtmXYToLatlon(double x, double y, int zone, bool southhemi,
   if (!(pj_utm = pj_init_plus(utm_dst.str().c_str()))) {
     return false;
   }
-  pj_transform(pj_utm, pj_latlon, 1, 1, &x, &y, NULL);
+  pj_transform(pj_utm, pj_latlon, 1, 1, &x, &y, nullptr);
   latlon->log = x;
   latlon->lat = y;
   pj_free(pj_latlon);

modules/localization/msf/local_map/ndt_map/ndt_map_matrix.cc

@@ -319,7 +319,7 @@ void NdtMapCells::Reduce(NdtMapCells* cell, const NdtMapCells& cell_new) {
 NdtMapMatrix::NdtMapMatrix() {
   rows_ = 0;
   cols_ = 0;
-  map3d_cells_ = NULL;
+  map3d_cells_ = nullptr;
 }
 
 NdtMapMatrix::~NdtMapMatrix() {
@@ -352,7 +352,7 @@ void NdtMapMatrix::Init(unsigned int rows, unsigned int cols) {
   if (map3d_cells_) {
     delete[] map3d_cells_;
   }
-  map3d_cells_ = NULL;
+  map3d_cells_ = nullptr;
   map3d_cells_ = new NdtMapCells[rows * cols];
   rows_ = rows;
   cols_ = cols;

modules/localization/ndt/README.md

@@ -15,7 +15,7 @@
   * Localization result defined by Protobuf message `LocalizationEstimate`, which can be found in file `localization/proto/localization.proto`. ( `/apollo/localization/pose`)
 
 ### NDT Localization Setting
-under some circumstance, we need to balance the speed and accuracy of the algorithm. So we expose some parameters of NDT matching process, It includes `online_resolution` for online pointcloud, `ndt_max_iterations` for iterative optimization of NDT matching, `ndt_target_resolution` for target resolution, `ndt_line_search_step_size` for searching step size of iteration and `ndt_transformation_epsilon` for convergence condition. 
+under some circumstance, we need to balance the speed and accuracy of the algorithm. So we expose some parameters of NDT matching process, It includes `online_resolution` for online pointcloud, `ndt_max_iterations` for iterative optimization of NDT matching, `ndt_target_resolution` for target resolution, `ndt_line_search_step_size` for searching step size of iteration and `ndt_transformation_epsilon` for convergence condition.
 
 ## Generate NDT Localization Map
   NDT Localization map is used for NDT-based localization, which is a voxel-grid representation of the environment. Each cell stores the centroid and relative covariance of the points in the cell. The map is organized as a group of map nodes. For more information, please refer to `apollo/modules/localization/msf/local_map/ndt_map`.

modules/localization/ndt/ndt_locator/lidar_locator_ndt.cc

@@ -428,7 +428,7 @@ void LidarLocatorNdt::ComposeMapCells(
       if (map_nodes_zones[y * 3 + x][2] - map_nodes_zones[y * 3 + x][0] >= 0 &&
           map_nodes_zones[y * 3 + x][3] - map_nodes_zones[y * 3 + x][1] >= 0) {
         // get map node
-        NdtMapNode* map_node_ptr = NULL;
+        NdtMapNode* map_node_ptr = nullptr;
         if (x == 0 && y == 0) {
           map_node_ptr = map_node_lt;
         } else {

modules/localization/ndt/ndt_locator/ndt_solver_test.cc

@@ -111,7 +111,7 @@ TEST_F(NdtSolverTestSuite, NdtSolver) {
   for (auto itr = buf.begin(); itr != buf.end(); ++itr, ++index) {
     msf::NdtMapNode* ndt_map_node =
         static_cast<msf::NdtMapNode*>(ndt_map.GetMapNodeSafe(*itr));
-    if (ndt_map_node == NULL) {
+    if (ndt_map_node == nullptr) {
       AERROR << "index: " << index << " is a NULL pointer!" << std::endl;
       continue;
     }

modules/localization/ndt/ndt_locator/ndt_voxel_grid_covariance.h

@@ -161,7 +161,7 @@ class VoxelGridCovariance {
   inline LeafConstPtr GetLeaf(int index) {
     typename std::map<size_t, Leaf>::iterator leaf_iter = leaves_.find(index);
     if (leaf_iter == leaves_.end()) {
-      return NULL;
+      return nullptr;
     }
     LeafConstPtr ret(&(leaf_iter->second));
     return ret;
@@ -189,7 +189,7 @@ class VoxelGridCovariance {
     // Find leaf associated with index
     typename std::map<size_t, Leaf>::iterator leaf_iter = leaves_.find(idx);
     if (leaf_iter == leaves_.end()) {
-      return NULL;
+      return nullptr;
     }
     // If such a leaf exists return the pointer to the leaf structure
     LeafConstPtr ret(&(leaf_iter->second));
@@ -216,7 +216,7 @@ class VoxelGridCovariance {
     // Find leaf associated with index
     typename std::map<size_t, Leaf>::iterator leaf_iter = leaves_.find(idx);
     if (leaf_iter == leaves_.end()) {
-      return NULL;
+      return nullptr;
     }
     // If such a leaf exists return the pointer to the leaf structure
     LeafConstPtr ret(&(leaf_iter->second));

modules/perception/README.md

@@ -44,7 +44,7 @@ The perception module outputs are:
 1. Set up the general settings in the configuration file `modules/perception/conf/perception_lowcost.conf`.
 2. Run the command  `./scripts/bootstrap.sh` to launch the web GUI.
 3. Select the vehicle model in the web GUI.
-4. Launch the perception module using the command `./scripts/perception_lowcost_vis.sh start` or by enabling the perception button on the *Module Controller* page of the web GUI. The command for stopping perception is `./scripts/perception_lowcost_vis.sh stop`. Note: please do not try to use GUI to enable perception but use script to stop it, vice versa. 
+4. Launch the perception module using the command `./scripts/perception_lowcost_vis.sh start` or by enabling the perception button on the *Module Controller* page of the web GUI. The command for stopping perception is `./scripts/perception_lowcost_vis.sh stop`. Note: please do not try to use GUI to enable perception but use script to stop it, vice versa.
 5. Download the demo data from the Apollo [Open Data Platform](http://data.apollo.auto).
 
 ```
@@ -59,7 +59,7 @@ Note:
 
     See [How to Run Perception Module on Your Local Computer](https://github.com/ApolloAuto/apollo/blob/master/docs/howto/how_to_run_perception_module_on_your_local_computer.md).
 
-3. This module contains a redistribution in binary form of a modified version of [caffe](https://github.com/BVLC/caffe). 
+3. This module contains a redistribution in binary form of a modified version of [caffe](https://github.com/BVLC/caffe).
 A copy of the caffe's original copyright statement is included below:
 
 ```

modules/perception/camera/common/timer.h

@@ -24,10 +24,10 @@ namespace camera {
 class Timer {
  public:
   Timer() { Tic(); }
-  void Tic() { gettimeofday(&start_tv_, NULL); }
+  void Tic() { gettimeofday(&start_tv_, nullptr); }
   uint64_t Toc() {
     struct timeval end_tv;
-    gettimeofday(&end_tv, NULL);
+    gettimeofday(&end_tv, nullptr);
     uint64_t elapsed = (end_tv.tv_sec - start_tv_.tv_sec) * 1000000 +
                        (end_tv.tv_usec - start_tv_.tv_usec);
     Tic();

modules/perception/camera/lib/lane/common/common_functions.h

@@ -46,7 +46,7 @@ bool PolyFit(const std::vector<Eigen::Matrix<Dtype, 2, 1>>& pos_vec,
              const int& order,
              Eigen::Matrix<Dtype, max_poly_order + 1, 1>* coeff,
              const bool& is_x_axis = true) {
-  if (coeff == NULL) {
+  if (coeff == nullptr) {
     AERROR << "The coefficient pointer is NULL.";
     return false;
   }
@@ -110,7 +110,7 @@ template <typename Dtype>
 bool RansacFitting(std::vector<Eigen::Matrix<Dtype, 2, 1>>* pos_vec,
                    Eigen::Matrix<Dtype, 4, 1>* coeff, const int max_iters = 100,
                    const int N = 5, Dtype inlier_thres = 0.1) {
-  if (coeff == NULL) {
+  if (coeff == nullptr) {
     AERROR << "The coefficient pointer is NULL.";
     return false;
   }

modules/perception/camera/test/camera_common_undistortion.cc

@@ -135,19 +135,19 @@ int ImageGpuPreprocessHandler::handle(uint8_t *src, uint8_t *dst) {
 int ImageGpuPreprocessHandler::release(void) {
   if (_d_mapy) {
     BASE_CUDA_CHECK(cudaFree(_d_mapy));
-    _d_mapy = NULL;
+    _d_mapy = nullptr;
   }
   if (_d_mapx) {
     BASE_CUDA_CHECK(cudaFree(_d_mapx));
-    _d_mapx = NULL;
+    _d_mapx = nullptr;
   }
   if (_d_dst) {
     BASE_CUDA_CHECK(cudaFree(_d_dst));
-    _d_dst = NULL;
+    _d_dst = nullptr;
   }
   if (_d_rgb) {
     BASE_CUDA_CHECK(cudaFree(_d_rgb));
-    _d_rgb = NULL;
+    _d_rgb = nullptr;
   }
   _inited = false;
   return 0;

modules/perception/camera/test/camera_common_undistortion.h

@@ -43,10 +43,10 @@ class ImageGpuPreprocessHandler {
                              int *height, std::vector<double> *D,
                              std::vector<double> *K);
 
-  float *_d_mapx = NULL;
-  float *_d_mapy = NULL;
-  uint8_t *_d_rgb = NULL;
-  uint8_t *_d_dst = NULL;
+  float *_d_mapx = nullptr;
+  float *_d_mapy = nullptr;
+  uint8_t *_d_rgb = nullptr;
+  uint8_t *_d_dst = nullptr;
 
   int _width = 0;     // image cols
   int _height = 0;    // image rows