added several grid-walks

added new helper methods/classes (e.g. for heading)
new test cases
optimize the dijkstra
cleanups/refactoring
added timed-benchmarks to the log
many more...

misc/KNNArray.h

@@ -0,0 +1,59 @@
+#ifndef KNNARRAY_H
+#define KNNARRAY_H
+
+/**
+ * this wrapper class provides all methods needed for nanoflanns KNN-search.
+ * in order for this wrapper class to work, your data-structure must provide
+ * the following methods:
+ *
+ * PointList:
+ *		size() - return the number of contained points
+ *		operator [] - access points via their index
+ * Point
+ *		operator [] - access each dimension via its index
+ *
+ * example:
+ *		std::vector<Point3> points;
+ *		KNNArray<std::vector<Point3>> arr(points);
+ *		KNN<KNNArray<std::vector<Point3>>, 3> knn(arr);
+ */
+template <typename T> class KNNArray {
+
+private:
+
+	/** the underlying data structure */
+	const T& elem;
+
+public:
+
+	/** ctor with the underlying data structure */
+	KNNArray(const T& elem) : elem(elem) {
+		;
+	}
+
+	/** get the number of elements to search throrugh */
+	inline int kdtree_get_point_count() const {
+		return elem.size();
+	}
+
+	/** use nanoflanns default bbox */
+	template <class BBOX> inline bool kdtree_get_bbox(BBOX& bb) const {
+		(void) bb; return false;
+	}
+
+	/** get the idx-th element's dim-th coordinate */
+	inline float kdtree_get_pt(const size_t idx, const int dim) const {
+		return elem[idx][dim];
+	}
+
+	/** get the SQUARED distance between the given coordinates and the provided element */
+	inline float kdtree_distance(const float* p1, const size_t idx_p2, size_t) const {
+		const float d0 = p1[0] - elem[idx_p2][0];
+		const float d1 = p1[1] - elem[idx_p2][1];
+		const float d2 = p1[2] - elem[idx_p2][2];
+		return (d0*d0) + (d1*d1) + (d2*d2);
+	}
+
+};
+
+#endif // KNNARRAY_H