Fix ball filter linear regression relative error bug (UBC-Thunderbots#3538)

* Fix ball filter linear regression using abs error

* Add comments that regression error is RMSE

* Fix typo

* Fix pass defender check for kick

* [pre-commit.ci lite] apply automatic fixes

---------

Co-authored-by: williamckha <williamha@outlook.com>
Co-authored-by: pre-commit-ci-lite[bot] <117423508+pre-commit-ci-lite[bot]@users.noreply.github.com>

Author: 3 people

src/software/ai/hl/stp/tactic/pass_defender/pass_defender_fsm.cpp

@@ -4,6 +4,7 @@
 #include "software/ai/evaluation/intercept.h"
 #include "software/ai/hl/stp/tactic/move_primitive.h"
 #include "software/geom/algorithms/closest_point.h"
+#include "software/geom/algorithms/contains.h"
 
 PassDefenderFSM::PassDefenderFSM(
     std::shared_ptr<const TbotsProto::AiConfig> ai_config_ptr)
@@ -18,6 +19,12 @@ bool PassDefenderFSM::passStarted(const Update& event)
         event.control_params.position_to_block_from.x() - ball_position.x(),
         event.control_params.position_to_block_from.y() - ball_position.y());
 
+    // Make sure ball is within playing area
+    if (!contains(event.common.world_ptr->field().fieldLines(), ball_position))
+    {
+        return false;
+    }
+
     bool pass_started = event.common.world_ptr->ball().hasBallBeenKicked(
         ball_receiver_point_vector.orientation(), MIN_PASS_SPEED,
         MAX_PASS_ANGLE_DIFFERENCE);

src/software/sensor_fusion/filter/ball_filter.cpp

@@ -2,7 +2,6 @@
 
 #include <Eigen/Dense>
 #include <algorithm>
-#include <limits>
 #include <vector>
 
 #include "shared/constants.h"
@@ -264,17 +263,9 @@ BallFilter::LinearRegressionResults BallFilter::calculateLinearRegression(
         A.bdcSvd(Eigen::ComputeThinU | Eigen::ComputeThinV).solve(b);
     // How to calculate the error is from
     // https://eigen.tuxfamily.org/dox/group__TutorialLinearAlgebra.html
-    double regression_error = std::numeric_limits<double>::max();
-
-    if ((A * regression_vector - b).norm() == 0 && b.norm() == 0)
-    {
-        regression_error = 0;
-    }
-    if (b.norm() != 0)
-    {
-        regression_error =
-            (A * regression_vector - b).norm() / (b.norm());  // norm() is L2 norm
-    }
+    // NOTE: using absolute error instead of relative because coordinates
+    // values should not affect error, also handles divide by 0 error
+    double regression_error = (A * regression_vector - b).norm();  // norm() is L2 norm
 
     // Find 2 points on the regression line that we solved for, and use this to construct
     // our own Line class

src/software/sensor_fusion/filter/ball_filter.h

@@ -43,7 +43,8 @@ class BallFilter
     static constexpr double MAX_BUFFER_SIZE_VELOCITY_MAGNITUDE = 4.0;
     // The extra amount beyond the ball's max speed that we treat ball detections as valid
     static constexpr double MAX_ACCEPTABLE_BALL_SPEED_BUFFER = 2.0;
-    // The maximum error threshold to considering using the generated linear regression
+    // The maximum root mean squared error threshold to considering using the generated
+    // linear regression.
     // TODO (#2752): Investigate different values of error threshold
     static constexpr double LINEAR_REGRESSION_ERROR_THRESHOLD = 1000.0;
 
@@ -85,6 +86,7 @@ class BallFilter
     struct LinearRegressionResults
     {
         Line regression_line;
+        // Regression error is root mean squared error
         double regression_error;
     };
 
@@ -131,7 +133,8 @@ class BallFilter
 
     /**
      * Given a buffer of ball detections, returns the line of best fit through
-     * the detection positions, and calculate the error of this regression.
+     * the detection positions, and calculate the root mean squared error of this
+     * regression.
      * Note: also considers vertical lines.
      *
      * @throws std::invalid_argument if ball_detections has less than 2 elements
@@ -145,7 +148,8 @@ class BallFilter
 
     /**
      * Given a list of ball detections, use linear regression to find a line of best fit
-     * through the ball positions, and calculate the error of this regression.
+     * through the ball positions, and calculate the root mean squared error of this
+     * regression.
      *
      * @throws std::invalid_argument if ball_detections has less than 2 elements
      *