Using external cameras to achieve robot localization has been widely proposed in the area of Intelligent Spaces. Recently, an online approach that simultaneously obtains robot's pose and its 3D structure using a single external camera has been developed [8]. Such proposal relies on a proper initialization of pose and structure information of the robot. The present paper proposes a solution to initialization which consists of retrieving 3D structure and motion of a rigid object from a set of point matches measured by the camera. A batch Structure from Motion (SFM) approach is proposed along a short path. By incorporating odometry information available in the robot, the ambiguity generated by a single view in the solution is solved. We propose to describe robot's motion and image detection as statistical processes in which the uncertainty is properly modelled. Using a Gaussian equivalence of the processes involved, the SFM cost function is expressed as a Maximum Likelihood optimization. The paper shows the improvements of the approach in the presence of the usual odometry drift noise, compared with those using Euclidean distance as a likelihood. The proposed method is assessed on synthetic and real data.