Fixed n<3 Jacobians
parent
3383e52c5f
commit
34db300802
|
@ -86,21 +86,29 @@ public:
|
|||
Circle2 circle1 = circles.front();
|
||||
boost::optional<Point2> best_fh;
|
||||
boost::optional<Circle2> best_circle;
|
||||
|
||||
// loop over all circles
|
||||
BOOST_FOREACH(const Circle2& it, circles) {
|
||||
// distance between circle centers.
|
||||
double d = circle1.center.dist(it.center);
|
||||
if (d < 1e-9)
|
||||
continue;
|
||||
continue; // skip circles that are in the same location
|
||||
// Find f and h, the intersection points in normalized circles
|
||||
boost::optional<Point2> fh = Point2::CircleCircleIntersection(
|
||||
circle1.radius / d, it.radius / d);
|
||||
// Check if this pair is better by checking h = fh->y()
|
||||
// if h is large, the intersections are well defined.
|
||||
if (fh && (!best_fh || fh->y() > best_fh->y())) {
|
||||
best_fh = fh;
|
||||
best_circle = it;
|
||||
}
|
||||
}
|
||||
|
||||
// use best fh to find actual intersection points
|
||||
std::list<Point2> solutions = Point2::CircleCircleIntersection(
|
||||
circle1.center, best_circle->center, best_fh);
|
||||
// TODO, pick winner based on other measurement
|
||||
|
||||
// pick winner based on other measurement
|
||||
return solutions.front();
|
||||
}
|
||||
|
||||
|
@ -110,30 +118,38 @@ public:
|
|||
virtual Vector unwhitenedError(const Values& x,
|
||||
boost::optional<std::vector<Matrix>&> H = boost::none) const {
|
||||
size_t n = size();
|
||||
if (H)
|
||||
assert(H->size()==n);
|
||||
Vector errors = zero(1);
|
||||
if (n >= 3) {
|
||||
if (n < 3) {
|
||||
if (H)
|
||||
// set Jacobians to zero for n<3
|
||||
for (size_t j = 0; j < n; j++)
|
||||
(*H)[j] = zeros(3, 1);
|
||||
return zero(1);
|
||||
} else {
|
||||
Vector error = zero(1);
|
||||
|
||||
// create n circles corresponding to measured range around each pose
|
||||
std::list<Circle2> circles;
|
||||
for (size_t j = 0; j < n; j++) {
|
||||
const Pose2& pose = x.at<Pose2>(keys_[j]);
|
||||
circles.push_back(Circle2(pose.translation(), measurements_[j]));
|
||||
}
|
||||
|
||||
// triangulate to get the optimized point
|
||||
// TODO: Should we have a (better?) variant that does this in relative coordinates ?
|
||||
Point2 optimizedPoint = triangulate(circles);
|
||||
|
||||
// TODO: triangulation should be followed by an optimization given poses
|
||||
// now evaluate the errors between predicted and measured range
|
||||
for (size_t j = 0; j < n; j++) {
|
||||
const Pose2& pose = x.at<Pose2>(keys_[j]);
|
||||
if (H)
|
||||
// also calculate 1*3 derivative for each of the n poses
|
||||
errors[0] += pose.range(optimizedPoint, (*H)[j]) - measurements_[j];
|
||||
error[0] += pose.range(optimizedPoint, (*H)[j]) - measurements_[j];
|
||||
else
|
||||
errors[0] += pose.range(optimizedPoint) - measurements_[j];
|
||||
error[0] += pose.range(optimizedPoint) - measurements_[j];
|
||||
}
|
||||
return error;
|
||||
}
|
||||
return errors;
|
||||
}
|
||||
|
||||
/// @return a deep copy of this factor
|
||||
|
|
|
@ -67,6 +67,11 @@ TEST( SmartRangeFactor, unwhitenedError ) {
|
|||
SmartRangeFactor f(sigma);
|
||||
f.addRange(1, r1);
|
||||
|
||||
// Check Jacobian for n==1
|
||||
vector<Matrix> H1(1);
|
||||
f.unwhitenedError(values, H1); // with H now !
|
||||
CHECK(assert_equal(zeros(3,1), H1.front()));
|
||||
|
||||
// Whenever there are two ranges or less, error should be zero
|
||||
Vector actual1 = f.unwhitenedError(values);
|
||||
EXPECT(assert_equal(Vector_(1,0.0), actual1));
|
||||
|
|
Loading…
Reference in New Issue