Autoware.Auto
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Namespaces | |
comparisons | |
detail | |
Classes | |
class | ByteReader |
A utility class to read byte vectors in big-endian order. More... | |
class | crtp |
struct | expression_valid |
struct | expression_valid< ExpressionTemplate, T, types::void_t< ExpressionTemplate< T > > > |
struct | expression_valid_with_return |
struct | expression_valid_with_return< ExpressionTemplate, T, ReturnT, std::enable_if_t< std::is_same< ReturnT, ExpressionTemplate< T > >::value > > |
class | LookupTable1D |
Functions | |
template<typename T > | |
constexpr T | wrap_angle (T angle) noexcept |
Wrap angle to the [-pi, pi] range. More... | |
template<typename T , std::int32_t kNumOfStates> | |
types::float32_t | calculate_squared_mahalanobis_distance (const Eigen::Matrix< T, kNumOfStates, 1 > &sample, const Eigen::Matrix< T, kNumOfStates, 1 > &mean, const Eigen::Matrix< T, kNumOfStates, kNumOfStates > &covariance_factor) |
Calculate square of mahalanobis distance. More... | |
template<typename T , std::int32_t kNumOfStates> | |
types::float32_t | calculate_mahalanobis_distance (const Eigen::Matrix< T, kNumOfStates, 1 > &sample, const Eigen::Matrix< T, kNumOfStates, 1 > &mean, const Eigen::Matrix< T, kNumOfStates, kNumOfStates > &covariance_factor) |
Calculate mahalanobis distance. More... | |
template<typename T > | |
T | lookup_1d (const std::vector< T > &domain, const std::vector< T > &range, const T value) |
types::float32_t autoware::common::helper_functions::calculate_mahalanobis_distance | ( | const Eigen::Matrix< T, kNumOfStates, 1 > & | sample, |
const Eigen::Matrix< T, kNumOfStates, 1 > & | mean, | ||
const Eigen::Matrix< T, kNumOfStates, kNumOfStates > & | covariance_factor | ||
) |
Calculate mahalanobis distance.
T | Type of elements in the matrix |
kNumOfStates | Number of states |
sample | Single column matrix containing sample whose distance needs to be computed |
mean | Single column matrix containing mean of samples received so far |
covariance_factor | Covariance matrix |
types::float32_t autoware::common::helper_functions::calculate_squared_mahalanobis_distance | ( | const Eigen::Matrix< T, kNumOfStates, 1 > & | sample, |
const Eigen::Matrix< T, kNumOfStates, 1 > & | mean, | ||
const Eigen::Matrix< T, kNumOfStates, kNumOfStates > & | covariance_factor | ||
) |
Calculate square of mahalanobis distance.
T | Type of elements in the matrix |
kNumOfStates | Number of states |
sample | Single column matrix containing sample whose distance needs to be computed |
mean | Single column matrix containing mean of samples received so far |
covariance_factor | Covariance matrix |
T autoware::common::helper_functions::lookup_1d | ( | const std::vector< T > & | domain, |
const std::vector< T > & | range, | ||
const T | value | ||
) |
Do a 1D table lookup: Does some semi-expensive O(N) error checking first. If query value fall out of the domain, then the value at the corresponding edge of the domain is returned.
[in] | domain | The domain, or set of x values |
[in] | range | The range, or set of y values |
[in] | value | The point in the domain to query, x |
std::domain_error | If domain or range is empty |
std::domain_error | If range is not the same size as domain |
std::domain_error | If domain is not sorted |
std::domain_error | If value is not finite (NAN or INF) |
T | The type of the function, must be interpolatable |
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constexprnoexcept |
Wrap angle to the [-pi, pi] range.
This method uses the formula suggested in the paper On wrapping the Kalman filter and estimating with the SO(2) group and implements the following formula: \(\mathrm{mod}(\alpha + \pi, 2 \pi) - \pi\).
[in] | angle | The input angle |
T | Type of scalar |