Lack of such criteria also makes it difficult to compare maps acquired by different mapping techniques and sensor types. Although the areas of sensing, measurement technology, and mapping have developed considerably and have been extended to 3-D over the recent years [9, 24�C26], assessment of the accuracy of the acquired maps and comparison between different mapping techniques is an important issue not extensively studied. In most mapping studies, the map accuracy is assessed by graphically displaying the acquired map together with the true map, and a subjective, qualitative judgment is made by visual comparison. The main contribution of this paper is the proposition of an objective and quantitative error criterion for the accuracy assessment and comparative evaluation of acquired maps.In Section 2., we give a description of the proposed error criterion and provide two other criteria for comparison: the Hausdorff metric and the median error. The use of the criterion is demonstrated through an example from ultrasonic and laser sensing in Section 3. Section 4. provides details of the experimental procedure and compares the results of the proposed criterion with the Hausdorff metric and the median error. Section 5. discusses the limiting circumstances for the criterion that may arise when there are temporal or spatial differences in acquiring the maps. The last section concludes the paper by indicating some potential application areas and providing directions for future research.2.?The Error CriterionLet P 3 and Q 3 be two finite sets of arbitrary points with N1 points in set P and N2 points in set Q. We do not require the correspondence between the two sets of points to be known. Each point set could found correspond to either (i) an acquired set of map points, (ii) discrete points corresponding to an absolute reference (the true map), or (iii) some curve (2-D) or shape (3-D) fit to the map points. The absolute reference could be an available true map or plan of the environment or could be acquired by making range or time-of-flight measurements through a very accurate sensing system.The well-known Euclidean distance d(pi, qj) : 3 �� ��0 of the i’th point in set P with position vector pi = (pxi, pyi, pzi)T to the j’th point qj = (qxj, qyj, qzj)T in set Q is given by:d(pi,qj)=(pxi?qxj)2+(pyi?qyj)2+(pzi?qzj)2i��1,��,N1j��1,��,N2(1)There is a choice of metrics to measure the similarity between two sets of points, each with certain advantages and disadvantages:A very simple metric is to take the minimum of the distances between any point of set P and any point of Q. This corresponds to a minimin function and is defined as:D(P,Q)=minpi��P{minqi��Qd(pi,qi)(2)In other words, for every point pi of set P, we find its minimum distance to any point qj of Q and we keep the minimum distance found among all points pi.