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beluga/include/beluga/sensor/likelihood_field_model_common.hpp
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| // Copyright 2022-2023 Ekumen, Inc. | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
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| #ifndef BELUGA_SENSOR_LIKELIHOOD_FIELD_MODEL_COMMON_HPP | ||
| #define BELUGA_SENSOR_LIKELIHOOD_FIELD_MODEL_COMMON_HPP | ||
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| #include <algorithm> | ||
| #include <cmath> | ||
| #include <random> | ||
| #include <range/v3/action/transform.hpp> | ||
| #include <sophus/se2.hpp> | ||
| #include <vector> | ||
| /** | ||
| * \file | ||
| * \brief Implementation of a likelihood field common sensor model for range finders. | ||
| */ | ||
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| namespace beluga { | ||
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| /// Parameters used to construct a LikelihoodFieldModelCommon instance. | ||
| /** | ||
| * See Probabilistic Robotics \cite thrun2005probabilistic Chapter 6.4, particularly Table 6.3. | ||
| */ | ||
| struct LikelihoodFieldModelCommonParam { | ||
| /// Maximum distance to obstacle. | ||
| /** | ||
| * When creating a distance map, if the distance to an obstacle is higher than the value specified here, | ||
| * then this value will be used. | ||
| */ | ||
| double max_obstacle_distance = 100.0; | ||
| /// Maximum range of a laser ray. | ||
| double max_laser_distance = 2.0; | ||
| /// Weight used to combine the probability of hitting an obstacle. | ||
| double z_hit = 0.5; | ||
| /// Weight used to combine the probability of random noise in perception. | ||
| double z_random = 0.5; | ||
| /// Standard deviation of a gaussian centered arounds obstacles. | ||
| /** | ||
| * Used to calculate the probability of the obstacle being hit. | ||
| */ | ||
| double sigma_hit = 0.2; | ||
| /// Whether to model unknown space or assume it free. | ||
| bool model_unknown_space = false; | ||
| }; | ||
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| /// Likelihood field common sensor model for range finders. | ||
| /** | ||
| * This model relies on a pre-computed likelihood map of the environment. | ||
| * It is less computationally intensive than the beluga::BeamSensorModel | ||
| * because no ray-tracing is required, and it can also provide better | ||
| * performance in environments with non-smooth occupation maps. See | ||
| * Probabilistic Robotics \cite thrun2005probabilistic, Chapter 6.4, | ||
| * for further reference. | ||
| * | ||
| * \note This class satisfies \ref SensorModelPage. | ||
| * | ||
| * \tparam OccupancyGrid Type representing an occupancy grid. | ||
| * It must satisfy \ref OccupancyGrid2Page. | ||
| */ | ||
| template <class OccupancyGrid> | ||
| class LikelihoodFieldModelCommon { | ||
| public: | ||
| /// Map representation type. | ||
| using map_type = OccupancyGrid; | ||
| /// Parameter type that the constructor uses to configure the likelihood field model. | ||
| using param_type = LikelihoodFieldModelCommonParam; | ||
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| /// Constructs a LikelihoodFieldCommonModel instance. | ||
| /** | ||
| * \param params Parameters to configure this instance. | ||
| * See beluga::LikelihoodFieldModelCommon for details. | ||
| * \param grid Occupancy grid representing the static map that the sensor model | ||
| * uses to compute a likelihood field for lidar hits and compute importance weights | ||
| * for particle states. | ||
| */ | ||
| explicit LikelihoodFieldModelCommon(const param_type& params, const map_type& grid) | ||
| : params_{params}, | ||
| likelihood_field_{make_likelihood_field(params, grid)}, | ||
| world_to_likelihood_field_transform_{grid.origin().inverse()} {} | ||
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| /// Returns the likelihood field, constructed from the provided map. | ||
| [[nodiscard]] const auto& likelihood_field() const { return likelihood_field_; } | ||
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| /// Update the sensor model with a new occupancy grid map. | ||
| /** | ||
| * This method re-computes the underlying likelihood field. | ||
| * | ||
| * \param grid New occupancy grid representing the static map. | ||
| */ | ||
| void update_map(const map_type& grid) { | ||
| likelihood_field_ = make_likelihood_field(params_, grid); | ||
| world_to_likelihood_field_transform_ = grid.origin().inverse(); | ||
| } | ||
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| protected: | ||
| param_type params_; | ||
| ValueGrid2<float> likelihood_field_; | ||
| Sophus::SE2d world_to_likelihood_field_transform_; | ||
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| static ValueGrid2<float> make_likelihood_field(const param_type& params, const OccupancyGrid& grid) { | ||
| const auto squared_distance = [&grid](std::size_t first, std::size_t second) { | ||
| return static_cast<float>((grid.coordinates_at(first) - grid.coordinates_at(second)).squaredNorm()); | ||
| }; | ||
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| /// Pre-computed variables | ||
| const double two_squared_sigma = 2 * params.sigma_hit * params.sigma_hit; | ||
| assert(two_squared_sigma > 0.0); | ||
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| const double amplitude = params.z_hit / (params.sigma_hit * std::sqrt(2 * Sophus::Constants<double>::pi())); | ||
| assert(amplitude > 0.0); | ||
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| const double offset = params.z_random / params.max_laser_distance; | ||
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| const auto to_likelihood = [amplitude, two_squared_sigma, offset](double squared_distance) { | ||
| return amplitude * std::exp(-squared_distance / two_squared_sigma) + offset; | ||
| }; | ||
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| const auto neighborhood = [&grid](std::size_t index) { return grid.neighborhood4(index); }; | ||
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| const auto squared_max_distance = static_cast<float>(params.max_obstacle_distance * params.max_obstacle_distance); | ||
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| // determine distances to obstacles and calculate likelihood values in-place | ||
| // to minimize memory usage when dealing with large maps | ||
| auto distance_map = | ||
| nearest_obstacle_distance_map(grid.obstacle_mask(), squared_distance, neighborhood, squared_max_distance); | ||
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| if (params.model_unknown_space) { | ||
| const auto inverse_max_distance = 1 / params.max_laser_distance; | ||
| const auto squared_background_distance = | ||
| -two_squared_sigma * std::log((inverse_max_distance - offset) / amplitude); | ||
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| distance_map |= beluga::actions::overlay( | ||
| grid.unknown_mask(), std::min(squared_max_distance, static_cast<float>(squared_background_distance))); | ||
| } | ||
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| auto likelihood_values = std::move(distance_map) | // | ||
| ranges::actions::transform(to_likelihood); | ||
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| return ValueGrid2<float>{std::move(likelihood_values), grid.width(), grid.resolution()}; | ||
| } | ||
| }; | ||
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| } // namespace beluga | ||
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| #endif |
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