Ceres::Covariance Compute Std::bad_alloc

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Introduction

In this article, we will delve into the issue of std::bad_alloc when computing covariance using the ceres::Covariance class in the OpenVINS library. The code snippet provided is a part of the OpenVINS library, which utilizes the Ceres Solver library for optimization and covariance computation. We will analyze the code, identify potential issues, and provide solutions to resolve the std::bad_alloc error.

Understanding the Code

The provided code snippet is responsible for computing the covariance of a state vector using the ceres::Covariance class. The state vector consists of orientation, position, velocity, and bias variables. The code creates a vector of pairs, where each pair represents a block of variables in the state vector. The ceres::Covariance class is then used to compute the covariance of these blocks.

Identifying the Issue

The error std::bad_alloc occurs when the program attempts to allocate memory that exceeds the available memory. In this case, the error occurs when calling the Compute method of the ceres::Covariance class. The Compute method takes two arguments: the vector of covariance blocks and a pointer to the problem object.

Analyzing the Code

Let's analyze the code snippet to identify potential issues:

std::vector<std::pair<const double *, const double *>> covariance_blocks;
int state_index = map_states[newest_cam_time];
// diagonals
covariance_blocks.push_back(std::make_pair(ceres_vars_ori[state_index], ceres_vars_ori[state_index]));
covariance_blocks.push_back(std::make_pair(ceres_vars_pos[state_index], ceres_vars_pos[state_index]));
covariance_blocks.push_back(std::make_pair(ceres_vars_vel[state_index], ceres_vars_vel[state_index]));
covariance_blocks.push_back(std::make_pair(ceres_vars_bias_g[state_index], ceres_vars_bias_g[state_index]));
covariance_blocks.push_back(std::make_pair(ceres_vars_bias_a[state_index], ceres_vars_bias_a[state_index]));
// orientation
covariance_blocks.push_back(std::make_pair(ceres_vars_ori[state_index], ceres_vars_pos[state_index]));
covariance_blocks.push_back(std::make_pair(ceres_vars_ori[state_index], ceres_vars_vel[state_index]));
covariance_blocks.push_back(std::make_pair(ceres_vars_ori[state_index], ceres_vars_bias_g[state_index]));
covariance_blocks.push_back(std::make_pair(ceres_vars_ori[state_index], ceres_vars_bias_a[state_index]));
// position
covariance_blocks.push_back(std::make_pair(ceres_vars_pos[state_index], ceres_vars_vel[state_index]));
covariance_blocks.push_back(std::make_pair(ceres_vars_pos[state_index], ceres_vars_bias_g[state_index]));
covariance_blocks.push_back(std::make_pair(ceres_vars_pos[state_index], ceres_vars_bias_a[state_index]));
// velocity
covariance_blocks.push_back(std::make_pair(ceres_vars_vel[state_index], ceres_vars_bias_g[state_index]));
covariance_blocks.push_back(std::make_pair(ceres_vars_vel[state_index], ceres_vars_bias_a[state_index]));
// bias_g
covariance_blocks.push_back(std::make_pair(ceres_vars_bias_g[state_index], ceres_vars_bias_a[state_index]));

// Finally, compute the covariance
ceres::Covariance::Options options_cov;
options_cov.null_space_rank = (!params.init_dyn_mle_opt_calib) * ((int)map_calib_cam2imu.size() * (6 + 8));
options_cov.min_reciprocal_condition_number = params.init_dyn_min_rec_cond;
// options_cov.algorithm_type = ceres::CovarianceAlgorithmType::DENSE_SVD;
options_cov.apply_loss_function = true; // Better consistency if we use this
options_cov.num_threads = params.init_dyn_mle_max_threads;
ceres::Covariance problem_cov(options_cov);
bool success = problem_cov.Compute(covariance_blocks, &problem);
if (!success) {
  PRINT_WARNING(YELLOW "[init-d]: covariance recovery failed...\n" RESET);
  free_state_memory();
  return false;
}

Potential Issues

  1. Memory Allocation: The ceres::Covariance class may be allocating memory that exceeds the available memory, leading to a std::bad_alloc error.
  2. Data Size: The size of the covariance blocks may be too large, causing the Compute method to fail.
  3. Algorithm Type: The algorithm type used by the ceres::Covariance class may not be suitable for the given problem, leading to a std::bad_alloc error.

Solutions

  1. Reduce Data Size: Reduce the size of the covariance blocks by removing unnecessary variables or using a more efficient data structure.
  2. Increase Memory Allocation: Increase the memory allocation limit using the std::set_new_handler function to handle memory allocation failures.
  3. Change Algorithm Type: Change the algorithm type used by the ceres::Covariance class to a more efficient one, such as ceres::CovarianceAlgorithmType::DENSE_SVD.

Conclusion

In conclusion, the std::bad_alloc error in the ceres::Covariance class can be caused by various factors, including memory allocation, data size, and algorithm type. By analyzing the code, identifying potential issues, and applying solutions, we can resolve the std::bad_alloc error and ensure the successful computation of covariance.

Recommendations

  1. Use a More Efficient Data Structure: Use a more efficient data structure, such as a sparse matrix, to represent the covariance blocks.
  2. Optimize Algorithm Type: Optimize the algorithm type used by the ceres::Covariance class to improve performance and reduce memory allocation.
  3. Increase Memory Allocation Limit: Increase the memory allocation limit using the std::set_new_handler function to handle memory allocation failures.

Future Work

  1. Investigate Alternative Covariance Computation Methods: Investigate alternative covariance computation methods, such as using a different library or implementing a custom solution.
  2. Optimize Code for Performance: Optimize the code for performance by reducing unnecessary computations and improving data structures.
  3. Implement Memory Management: Implement memory management techniques, such as memory pooling or caching, to reduce memory allocation and deallocation overhead.
    ceres::Covariance Compute std::bad_alloc: A Comprehensive Q&A =================================================================

Introduction

In our previous article, we analyzed the issue of std::bad_alloc when computing covariance using the ceres::Covariance class in the OpenVINS library. We identified potential issues, such as memory allocation, data size, and algorithm type, and provided solutions to resolve the std::bad_alloc error. In this article, we will provide a Q&A section to address common questions and concerns related to the ceres::Covariance class and its usage.

Q&A

Q: What is the ceres::Covariance class, and what is its purpose?

A: The ceres::Covariance class is a part of the Ceres Solver library, which is used for optimization and covariance computation. Its purpose is to compute the covariance of a state vector, which represents the uncertainty of the state variables.

Q: What are the common causes of std::bad_alloc errors in the ceres::Covariance class?

A: The common causes of std::bad_alloc errors in the ceres::Covariance class are:

  • Memory allocation: The ceres::Covariance class may be allocating memory that exceeds the available memory.
  • Data size: The size of the covariance blocks may be too large, causing the Compute method to fail.
  • Algorithm type: The algorithm type used by the ceres::Covariance class may not be suitable for the given problem, leading to a std::bad_alloc error.

Q: How can I reduce the memory allocation in the ceres::Covariance class?

A: To reduce the memory allocation in the ceres::Covariance class, you can:

  • Reduce the size of the covariance blocks by removing unnecessary variables or using a more efficient data structure.
  • Increase the memory allocation limit using the std::set_new_handler function to handle memory allocation failures.
  • Change the algorithm type used by the ceres::Covariance class to a more efficient one, such as ceres::CovarianceAlgorithmType::DENSE_SVD.

Q: What are the benefits of using the ceres::Covariance class?

A: The benefits of using the ceres::Covariance class are:

  • Efficient covariance computation: The ceres::Covariance class provides an efficient way to compute the covariance of a state vector.
  • Robustness: The ceres::Covariance class is designed to handle large and complex problems, making it a robust choice for covariance computation.
  • Flexibility: The ceres::Covariance class provides a flexible way to customize the covariance computation process, allowing users to choose the algorithm type and other options.

Q: How can I troubleshoot std::bad_alloc errors in the ceres::Covariance class?

A: To troubleshoot std::bad_alloc errors in the ceres::Covariance class, you can:

  • Check the memory allocation limit: Verify that the memory allocation limit is set correctly and that the ceres::Covariance class is not exceeding it.
  • Analyze the data size: Check the size of the covariance blocks and ensure that it is not too large.
  • Change the algorithm type: Try changing the algorithm type used by the ceres::Covariance class to a more efficient one.

Q: What are the best practices for using the ceres::Covariance class?

A: The best practices for using the ceres::Covariance class are:

  • Use a more efficient data structure: Use a more efficient data structure, such as a sparse matrix, to represent the covariance blocks.
  • Optimize the algorithm type: Optimize the algorithm type used by the ceres::Covariance class to improve performance and reduce memory allocation.
  • Increase the memory allocation limit: Increase the memory allocation limit using the std::set_new_handler function to handle memory allocation failures.

Conclusion

In conclusion, the ceres::Covariance class is a powerful tool for covariance computation, but it requires careful usage to avoid std::bad_alloc errors. By understanding the common causes of std::bad_alloc errors and following the best practices for using the ceres::Covariance class, users can ensure efficient and robust covariance computation.

Recommendations

  1. Use a More Efficient Data Structure: Use a more efficient data structure, such as a sparse matrix, to represent the covariance blocks.
  2. Optimize Algorithm Type: Optimize the algorithm type used by the ceres::Covariance class to improve performance and reduce memory allocation.
  3. Increase Memory Allocation Limit: Increase the memory allocation limit using the std::set_new_handler function to handle memory allocation failures.

Future Work

  1. Investigate Alternative Covariance Computation Methods: Investigate alternative covariance computation methods, such as using a different library or implementing a custom solution.
  2. Optimize Code for Performance: Optimize the code for performance by reducing unnecessary computations and improving data structures.
  3. Implement Memory Management: Implement memory management techniques, such as memory pooling or caching, to reduce memory allocation and deallocation overhead.