This paper describes the participation of Idiap-MULTI to the Robot Vision Task at imageCLEF 2010. Our approach was based on a discriminative classification algorithm using multiple cues. Specically, we used an SVM and combined up to four different histogram-based features with the kernel averaging method. We considered as output of the classifier, for each frame, the label and its associated margin, which we took as a measure of the confidence of the decision. If the margin value is below a threshold, determined via cross-validation during training, the classier abstains from assigning a label to the incoming frame. This method was submitted to the obligatory task, obtaining a maximum score of up to 662, which ranked second in the overall competition. We then extended this algorithm for the optional task, where it is possible to exploit the temporal continuity of the sequence. We implemented a door detector so to infer when the robot has entered a new room. Then, we designed a stability estimation algorithm for determining the label of the room where the robot has entered, and we used this knowledge as a prior for the upcoming frames. Our approach obtained a score of up to 2052 in the obligatory task, ranking first.