diff --git a/Localization/histogram_filter/animation.gif b/Localization/histogram_filter/animation.gif
new file mode 100644
index 00000000..d868c411
Binary files /dev/null and b/Localization/histogram_filter/animation.gif differ
diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py
index f8a8be06..ed1f721a 100644
--- a/Localization/histogram_filter/histogram_filter.py
+++ b/Localization/histogram_filter/histogram_filter.py
@@ -33,7 +33,6 @@ MINY = -5.0
 MAXX = 15.0
 MAXY = 25.0
 
-
 # simulation paramters
 NOISE_RANGE = 2.0  # [m] 1σ range noise parameter
 NOISE_SPEED = 0.5  # [m/s] 1σ speed noise parameter
diff --git a/README.md b/README.md
index 24baf5d6..e2e4d83c 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,8 @@ Python codes for robotics algorithm.
    * [Localization](#localization)
       * [Extended Kalman Filter localization](#extended-kalman-filter-localization)
       * [Unscented Kalman Filter localization](#unscented-kalman-filter-localization)
-      * [Particle Filter localization](#particle-filter-localization)
+      * [Particle filter localization](#particle-filter-localization)
+      * [Histogram filter localization](#histogram-filter-localization)
    * [Mapping](#mapping)
       * [Gaussian grid map](#gaussian-grid-map)
       * [Ray casting grid map](#ray-casting-grid-map)
@@ -97,6 +98,10 @@ the green point is positioning observation (ex. GPS), and the red line is estima
 
 The red ellipse is estimated covariance ellipse with EKF.
 
+Ref:
+
+- [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
+
 ## Unscented Kalman Filter localization
 
 ![2](https://github.com/AtsushiSakai/PythonRobotics/raw/master/Localization/unscented_kalman_filter/animation.gif)
@@ -109,7 +114,8 @@ Ref:
 
 - [Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization](https://www.researchgate.net/publication/267963417_Discriminatively_Trained_Unscented_Kalman_Filter_for_Mobile_Robot_Localization)
 
-## Particle Filter localization
+
+## Particle filter localization
 
 ![2](https://github.com/AtsushiSakai/PythonRobotics/raw/master/Localization/particle_filter/animation.gif)
 
@@ -123,6 +129,27 @@ It is assumed that the robot can measure a distance from landmarks (RFID).
 
 This measurements are used for PF localization.
 
+Ref:
+
+- [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
+
+
+## Histogram filter localization
+
+![3](https://github.com/AtsushiSakai/PythonRobotics/raw/master/Localization/histogram_filter/animation.gif)
+
+This is a 2D localization example with Histogram filter.
+
+In this simulation, x,y are unknown, yaw is known.
+
+The filter integrates speed input and range observations from RFID for localization.
+
+Initial position is not needed.
+
+Ref:
+
+- [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
+
 # Mapping
 
 ## Gaussian grid map
@@ -575,3 +602,4 @@ Atsushi Sakai ([@Atsushi_twi](https://twitter.com/Atsushi_twi))
 
 
 
+
diff --git a/tests/test_histogram_filter.py b/tests/test_histogram_filter.py
new file mode 100644
index 00000000..f39995b2
--- /dev/null
+++ b/tests/test_histogram_filter.py
@@ -0,0 +1,12 @@
+from unittest import TestCase
+
+from Localization.histogram_filter import histogram_filter as m
+
+print(__file__)
+
+
+class Test(TestCase):
+
+    def test1(self):
+        m.show_animation = False
+        m.main()