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Avoid auto for Eigen expressions. (#899) 

While harmless in most cases, auto can delay evaluation
of expressions in unexpected ways.
So it is better to avoid auto for Eigen expressions.
https://eigen.tuxfamily.org/dox/TopicPitfalls.html
master
gaschler 2018-02-13 12:34:43 +01:00 committed by Wally B. Feed
parent 2a74484be1
commit 7448f93b50
13 changed files with 20 additions and 17 deletions
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2
cartographer/internal/mapping_3d/local_trajectory_builder.cc
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@ -229,7 +229,7 @@ LocalTrajectoryBuilder::InsertIntoSubmap(
}
active_submaps_.InsertRangeData(filtered_range_data_in_local,
gravity_alignment);
const auto rotational_scan_matcher_histogram =
const Eigen::VectorXf rotational_scan_matcher_histogram =
scan_matching::RotationalScanMatcher::ComputeHistogram(
sensor::TransformPointCloud(
filtered_range_data_in_tracking.returns,

5
cartographer/io/outlier_removing_points_processor.cc
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@ -94,7 +94,7 @@ void OutlierRemovingPointsProcessor::ProcessInPhaseTwo(
const Eigen::Vector3f delta = batch.points[i] - batch.origin;
const float length = delta.norm();
for (float x = 0; x < length; x += voxel_size_) {
const auto index =
const Eigen::Array3i index =
voxels_.GetCellIndex(batch.origin + (x / length) * delta);
if (voxels_.value(index).hits > 0) {
++voxels_.mutable_value(index)->rays;
@ -108,7 +108,8 @@ void OutlierRemovingPointsProcessor::ProcessInPhaseThree(
constexpr double kMissPerHitLimit = 3;
std::unordered_set<int> to_remove;
for (size_t i = 0; i < batch->points.size(); ++i) {
const auto voxel = voxels_.value(voxels_.GetCellIndex(batch->points[i]));
const VoxelData voxel =
voxels_.value(voxels_.GetCellIndex(batch->points[i]));
if (!(voxel.rays < kMissPerHitLimit * voxel.hits)) {
to_remove.insert(i);
}

4
cartographer/io/probability_grid_points_processor.cc
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@ -134,9 +134,9 @@ std::unique_ptr<Image> DrawProbabilityGrid(
}
auto image = common::make_unique<Image>(cell_limits.num_x_cells,
cell_limits.num_y_cells);
for (const auto& xy_index :
for (const Eigen::Array2i& xy_index :
cartographer::mapping_2d::XYIndexRangeIterator(cell_limits)) {
const auto index = xy_index + *offset;
const Eigen::Array2i index = xy_index + *offset;
constexpr uint8 kUnknownValue = 128;
const uint8 value =
probability_grid.IsKnown(index)

3
cartographer/io/xray_points_processor.cc
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@ -149,7 +149,8 @@ void XRayPointsProcessor::WriteVoxels(const Aggregation& aggregation,
}
// Returns the (x, y) pixel of the given 'index'.
const auto voxel_index_to_pixel = [this](const Eigen::Array3i& index) {
const auto voxel_index_to_pixel =
[this](const Eigen::Array3i& index) -> Eigen::Array2i {
// We flip the y axis, since matrices rows are counted from the top.
return Eigen::Array2i(bounding_box_.max()[1] - index[1],
bounding_box_.max()[2] - index[2]);

2
cartographer/mapping_3d/scan_matching/ceres_scan_matcher_test.cc
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@ -37,7 +37,7 @@ class CeresScanMatcherTest : public ::testing::Test {
: hybrid_grid_(1.f),
expected_pose_(
transform::Rigid3d::Translation(Eigen::Vector3d(-1., 0., 0.))) {
for (const auto& point :
for (const Eigen::Vector3f& point :
{Eigen::Vector3f(-3.f, 2.f, 0.f), Eigen::Vector3f(-4.f, 2.f, 0.f),
Eigen::Vector3f(-5.f, 2.f, 0.f), Eigen::Vector3f(-6.f, 2.f, 0.f),
Eigen::Vector3f(-6.f, 3.f, 1.f), Eigen::Vector3f(-6.f, 4.f, 2.f),

2
cartographer/mapping_3d/scan_matching/interpolated_grid_test.cc
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@ -29,7 +29,7 @@ class InterpolatedGridTest : public ::testing::Test {
protected:
InterpolatedGridTest()
: hybrid_grid_(0.1f), interpolated_grid_(hybrid_grid_) {
for (const auto& point :
for (const Eigen::Vector3f& point :
{Eigen::Vector3f(-3.f, 2.f, 0.f), Eigen::Vector3f(-4.f, 2.f, 0.f),
Eigen::Vector3f(-5.f, 2.f, 0.f), Eigen::Vector3f(-6.f, 2.f, 0.f),
Eigen::Vector3f(-6.f, 3.f, 1.f), Eigen::Vector3f(-6.f, 4.f, 2.f),

3
cartographer/mapping_3d/scan_matching/low_resolution_matcher.cc
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@ -24,7 +24,8 @@ std::function<float(const transform::Rigid3f&)> CreateLowResolutionMatcher(
const HybridGrid* low_resolution_grid, const sensor::PointCloud* points) {
return [=](const transform::Rigid3f& pose) {
float score = 0.f;
for (const auto& point : sensor::TransformPointCloud(*points, pose)) {
for (const Eigen::Vector3f& point :
sensor::TransformPointCloud(*points, pose)) {
// TODO(zhengj, whess): Interpolate the Grid to get better score.
score += low_resolution_grid->GetProbability(
low_resolution_grid->GetCellIndex(point));

2
cartographer/mapping_3d/scan_matching/real_time_correlative_scan_matcher_test.cc
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@ -39,7 +39,7 @@ class RealTimeCorrelativeScanMatcherTest : public ::testing::Test {
: hybrid_grid_(0.1f),
expected_pose_(Eigen::Vector3d(-1., 0., 0.),
Eigen::Quaterniond::Identity()) {
for (const auto& point :
for (const Eigen::Vector3f& point :
{Eigen::Vector3f(-3.f, 2.f, 0.f), Eigen::Vector3f(-4.f, 2.f, 0.f),
Eigen::Vector3f(-5.f, 2.f, 0.f), Eigen::Vector3f(-6.f, 2.f, 0.f),
Eigen::Vector3f(-6.f, 3.f, 1.f), Eigen::Vector3f(-6.f, 4.f, 2.f),

2
cartographer/sensor/compressed_point_cloud_test.cc
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@ -106,7 +106,7 @@ TEST(CompressPointCloudTest, CompressesNoGaps) {
EXPECT_EQ(decompressed.size(), recompressed.size());
std::vector<float> x_coord;
for (const auto& p : compressed) {
for (const Eigen::Vector3f& p : compressed) {
x_coord.push_back(p[0]);
}
std::sort(x_coord.begin(), x_coord.end());

4
cartographer/sensor/point_cloud.cc
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@ -48,7 +48,7 @@ TimedPointCloud TransformTimedPointCloud(const TimedPointCloud& point_cloud,
PointCloud CropPointCloud(const PointCloud& point_cloud, const float min_z,
const float max_z) {
PointCloud cropped_point_cloud;
for (const auto& point : point_cloud) {
for (const Eigen::Vector3f& point : point_cloud) {
if (min_z <= point.z() && point.z() <= max_z) {
cropped_point_cloud.push_back(point);
}
@ -59,7 +59,7 @@ PointCloud CropPointCloud(const PointCloud& point_cloud, const float min_z,
TimedPointCloud CropTimedPointCloud(const TimedPointCloud& point_cloud,
const float min_z, const float max_z) {
TimedPointCloud cropped_point_cloud;
for (const auto& point : point_cloud) {
for (const Eigen::Vector4f& point : point_cloud) {
if (min_z <= point.z() && point.z() <= max_z) {
cropped_point_cloud.push_back(point);
}

4
cartographer/sensor/range_data.cc
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@ -58,11 +58,11 @@ proto::RangeData ToProto(const RangeData& range_data) {
proto::RangeData proto;
*proto.mutable_origin() = transform::ToProto(range_data.origin);
proto.mutable_returns()->Reserve(range_data.returns.size());
for (const auto& point : range_data.returns) {
for (const Eigen::Vector3f& point : range_data.returns) {
*proto.add_returns() = transform::ToProto(point);
}
proto.mutable_misses()->Reserve(range_data.misses.size());
for (const auto& point : range_data.misses) {
for (const Eigen::Vector3f& point : range_data.misses) {
*proto.add_misses() = transform::ToProto(point);
}
return proto;

2
cartographer/sensor/timed_point_cloud_data.cc
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@ -28,7 +28,7 @@ proto::TimedPointCloudData ToProto(
proto.set_timestamp(common::ToUniversal(timed_point_cloud_data.time));
*proto.mutable_origin() = transform::ToProto(timed_point_cloud_data.origin);
proto.mutable_point_data()->Reserve(timed_point_cloud_data.ranges.size());
for (const auto& range : timed_point_cloud_data.ranges) {
for (const Eigen::Vector4f& range : timed_point_cloud_data.ranges) {
*proto.add_point_data() = transform::ToProto(range);
}
return proto;

2
cartographer/sensor/voxel_filter.cc
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@ -81,7 +81,7 @@ PointCloudType FilterPointCloudUsingVoxels(
const PointCloudType& point_cloud,
mapping_3d::HybridGridBase<uint8>* voxels) {
PointCloudType results;
for (const auto& point : point_cloud) {
for (const typename PointCloudType::value_type& point : point_cloud) {
auto* const value =
voxels->mutable_value(voxels->GetCellIndex(point.template head<3>()));
if (*value == 0) {