install

install by apt

install on ubuntu20

sudo apt install libceres-dev

install from source

enable eigen_metis during ceres compilation

prerequisites

  1. Ceres version ≥ 2.1.0 (METIS support added in 2.1).
  2. Eigen ≥ 3.3.0
  3. METIS library installed (required by EIGEN_METIS).

Add configuration to compilation: -DEIGENSPARSE=ON, -DEIGEN_METIS=ON.

enable cuda

Prerequisites

  1. CUDA Toolkit ≥ 9.0
  2. CMake ≥ 3.5
  3. g++ ≥ 7

Add configuration to compilation: -DCERES_USE_CUDA=ON

In the CMake output (or CMakeCache.txt), ensure:

-- CUDA support     : YES

check ceres

check version of ceres

sudo cat /usr/local/include/ceres/version.h

usage

problem

Problem problem;

// Add residual terms to the problem using the autodiff wrapper to get the derivatives automatically.
problem.AddResidualBlock(new AutoDiffCostFunction<F1, 1, 1, 1>(new F1), nullptr, &x1, &x2);
// AutoDiffCostFunction<函数，残差的数量，第一个参数块，第二个参数块>

options

ceres::Solver::Options options;

// Do not print the iteration table to stdout
options.minimizer_progress_to_stdout = false;

// Do not log per-iteration progress via glog
options.logging_type = ceres::SILENT;

// If you added any callbacks that print, remove them.
// options.callbacks.clear();

robust loss function

Loss Function Behavior for Large Residuals Notes
HuberLoss Linear growth Mild robustness
SoftLOneLoss Very smooth, gentle Often good default
CauchyLoss Logarithmic growth Strong outlier suppression
TukeyLoss Hard rejection Non-convex, aggressive
CauchyLoss

Interpretation of Parameter

Small

  1. Strong outlier suppression
  2. Risk of discarding valid but large residuals

Large:

  1. Behavior closer to standard least squares
  2. Weaker robustness

A common practical choice is to set parameter close to the expected inlier residual magnitude. For SLAM / BA, Cauchy is often better than Huber when mismatches exist.

HuberLoss