A low-flying autonomous rotorcraft traveling in an unknown domain must use passive sensors to detect obstacles and form a three-dimensional model of its environment. As the rotorcraft travels toward a predefined destination, it acquires images of stationary objects in its field of view. Several texture classifying operators are applied to the original intensity images to obtain "texture" images. The application of each operator to the sequence of images will form an alternate sequence of images in which pixel values encode a measure of texture. In our approach to reconstruct the environment, we divide the three dimensional space of interest ( i.e. the environment) into small cubic volumetric elements (voxels). It is assumed that the position and orientation of the the camera with respect to the environment is known. Thus, for every pixel in each image in a sequence, we can compute a ray originating at the camera center and extending through and beyond the pixel. The value observed at the pixel is assigned as an observation for all the voxels through which the ray passes. Then, using the mean and variance of the observations for each voxel, one can determine whether the voxel is full or empty. Each sequence of images is used to form a three dimensional model of the environment. The reconstruction obtained using the sequence of intensity images is not necessarily the same as that obtained using texture images. While intensity images may do well in one area of the scene, texture images may do well in others. Thus, by fusing the different environment models together, a more robust model of the environment is formed. We discuss various methods of fusing the environment models obtained using intensity as well as texture measures and discuss the advantages and disadvantages of each as related to our application. Finally, we present experimental results obtained using real image sequences.