diff --git a/perception/lidar_centerpoint/lib/network/network_trt.cpp b/perception/lidar_centerpoint/lib/network/network_trt.cpp
index 88319ff51fe35..2d841d22c2eb1 100644
--- a/perception/lidar_centerpoint/lib/network/network_trt.cpp
+++ b/perception/lidar_centerpoint/lib/network/network_trt.cpp
@@ -14,6 +14,8 @@
 
 #include "lidar_centerpoint/network/network_trt.hpp"
 
+#include <rclcpp/rclcpp.hpp>
+
 namespace centerpoint
 {
 bool VoxelEncoderTRT::setProfile(
@@ -59,6 +61,15 @@ bool HeadTRT::setProfile(
 
   for (std::size_t ci = 0; ci < out_channel_sizes_.size(); ci++) {
     auto out_name = network.getOutput(ci)->getName();
+
+    if (
+      out_name == std::string("heatmap") &&
+      network.getOutput(ci)->getDimensions().d[1] != static_cast<int32_t>(out_channel_sizes_[ci])) {
+      RCLCPP_ERROR(
+        rclcpp::get_logger("lidar_centerpoint"),
+        "Expected and actual number of classes do not match");
+      return false;
+    }
     auto out_dims = nvinfer1::Dims4(
       config_.batch_size_, out_channel_sizes_[ci], config_.down_grid_size_y_,
       config_.down_grid_size_x_);
diff --git a/perception/lidar_centerpoint/lib/network/tensorrt_wrapper.cpp b/perception/lidar_centerpoint/lib/network/tensorrt_wrapper.cpp
index 079c41d06c6e0..4840b63940df1 100644
--- a/perception/lidar_centerpoint/lib/network/tensorrt_wrapper.cpp
+++ b/perception/lidar_centerpoint/lib/network/tensorrt_wrapper.cpp
@@ -14,6 +14,8 @@
 
 #include "lidar_centerpoint/network/tensorrt_wrapper.hpp"
 
+#include <rclcpp/rclcpp.hpp>
+
 #include <NvOnnxParser.h>
 
 #include <fstream>
@@ -38,7 +40,7 @@ bool TensorRTWrapper::init(
   runtime_ =
     tensorrt_common::TrtUniquePtr<nvinfer1::IRuntime>(nvinfer1::createInferRuntime(logger_));
   if (!runtime_) {
-    std::cout << "Fail to create runtime" << std::endl;
+    RCLCPP_ERROR(rclcpp::get_logger("lidar_centerpoint"), "Failed to create runtime");
     return false;
   }
 
@@ -57,14 +59,15 @@ bool TensorRTWrapper::init(
 bool TensorRTWrapper::createContext()
 {
   if (!engine_) {
-    std::cout << "Fail to create context: Engine isn't created" << std::endl;
+    RCLCPP_ERROR(
+      rclcpp::get_logger("lidar_centerpoint"), "Failed to create context: Engine was not created");
     return false;
   }
 
   context_ =
     tensorrt_common::TrtUniquePtr<nvinfer1::IExecutionContext>(engine_->createExecutionContext());
   if (!context_) {
-    std::cout << "Fail to create context" << std::endl;
+    RCLCPP_ERROR(rclcpp::get_logger("lidar_centerpoint"), "Failed to create context");
     return false;
   }
 
@@ -78,14 +81,14 @@ bool TensorRTWrapper::parseONNX(
   auto builder =
     tensorrt_common::TrtUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(logger_));
   if (!builder) {
-    std::cout << "Fail to create builder" << std::endl;
+    RCLCPP_ERROR(rclcpp::get_logger("lidar_centerpoint"), "Failed to create builder");
     return false;
   }
 
   auto config =
     tensorrt_common::TrtUniquePtr<nvinfer1::IBuilderConfig>(builder->createBuilderConfig());
   if (!config) {
-    std::cout << "Fail to create config" << std::endl;
+    RCLCPP_ERROR(rclcpp::get_logger("lidar_centerpoint"), "Failed to create config");
     return false;
   }
 #if (NV_TENSORRT_MAJOR * 1000) + (NV_TENSORRT_MINOR * 100) + NV_TENSOR_PATCH >= 8400
@@ -95,10 +98,12 @@ bool TensorRTWrapper::parseONNX(
 #endif
   if (precision == "fp16") {
     if (builder->platformHasFastFp16()) {
-      std::cout << "use TensorRT FP16 Inference" << std::endl;
+      RCLCPP_INFO(rclcpp::get_logger("lidar_centerpoint"), "Using TensorRT FP16 Inference");
       config->setFlag(nvinfer1::BuilderFlag::kFP16);
     } else {
-      std::cout << "TensorRT FP16 Inference isn't supported in this environment" << std::endl;
+      RCLCPP_INFO(
+        rclcpp::get_logger("lidar_centerpoint"),
+        "TensorRT FP16 Inference isn't supported in this environment");
     }
   }
 
@@ -107,7 +112,7 @@ bool TensorRTWrapper::parseONNX(
   auto network =
     tensorrt_common::TrtUniquePtr<nvinfer1::INetworkDefinition>(builder->createNetworkV2(flag));
   if (!network) {
-    std::cout << "Fail to create network" << std::endl;
+    RCLCPP_ERROR(rclcpp::get_logger("lidar_centerpoint"), "Failed to create network");
     return false;
   }
 
@@ -116,22 +121,23 @@ bool TensorRTWrapper::parseONNX(
   parser->parseFromFile(onnx_path.c_str(), static_cast<int>(nvinfer1::ILogger::Severity::kERROR));
 
   if (!setProfile(*builder, *network, *config)) {
-    std::cout << "Fail to set profile" << std::endl;
+    RCLCPP_ERROR(rclcpp::get_logger("lidar_centerpoint"), "Failed to set profile");
     return false;
   }
 
-  std::cout << "Applying optimizations and building TRT CUDA engine (" << onnx_path << ") ..."
-            << std::endl;
+  RCLCPP_INFO_STREAM(
+    rclcpp::get_logger("lidar_centerpoint"),
+    "Applying optimizations and building TRT CUDA engine (" << onnx_path << ") ...");
   plan_ = tensorrt_common::TrtUniquePtr<nvinfer1::IHostMemory>(
     builder->buildSerializedNetwork(*network, *config));
   if (!plan_) {
-    std::cout << "Fail to create serialized network" << std::endl;
+    RCLCPP_ERROR(rclcpp::get_logger("lidar_centerpoint"), "Failed to create serialized network");
     return false;
   }
   engine_ = tensorrt_common::TrtUniquePtr<nvinfer1::ICudaEngine>(
     runtime_->deserializeCudaEngine(plan_->data(), plan_->size()));
   if (!engine_) {
-    std::cout << "Fail to create engine" << std::endl;
+    RCLCPP_ERROR(rclcpp::get_logger("lidar_centerpoint"), "Failed to create engine");
     return false;
   }
 
@@ -140,7 +146,7 @@ bool TensorRTWrapper::parseONNX(
 
 bool TensorRTWrapper::saveEngine(const std::string & engine_path)
 {
-  std::cout << "Writing to " << engine_path << std::endl;
+  RCLCPP_INFO_STREAM(rclcpp::get_logger("lidar_centerpoint"), "Writing to " << engine_path);
   std::ofstream file(engine_path, std::ios::out | std::ios::binary);
   file.write(reinterpret_cast<const char *>(plan_->data()), plan_->size());
   return true;
@@ -154,7 +160,7 @@ bool TensorRTWrapper::loadEngine(const std::string & engine_path)
   std::string engine_str = engine_buffer.str();
   engine_ = tensorrt_common::TrtUniquePtr<nvinfer1::ICudaEngine>(runtime_->deserializeCudaEngine(
     reinterpret_cast<const void *>(engine_str.data()), engine_str.size()));
-  std::cout << "Loaded engine from " << engine_path << std::endl;
+  RCLCPP_INFO_STREAM(rclcpp::get_logger("lidar_centerpoint"), "Loaded engine from " << engine_path);
   return true;
 }