This paper presents a new approach to the management of the environmental map for mobile robots in dynamic environments. The environmental map is built of primitive features, such as lines, points, and even circles, extracted from ambiguous data captured by the robot's sonar sensor ring. The feature map must be managed because the indoor surroundings where mobile robots operate are continuously changing due to nonstationary objects, such as wastebaskets, tables, and people. The features are processed by trimming, division, or removal, depending on the dynamic circumstances. All processing refers to the occupancy probabilities of grid squares generated for the map features. The occupancy probabilities of the squares are updated using the Bayesian updating model with the sonar sensor data. Experimental results demonstrate the validity of the proposed method.