Stereo is a key component in many autonomous navigation tasks. These applications demand real-time performance and consequently, the state-of-the-art uses local correlation-based algorithms that lend themselves to algorithmic and hardware optimization. These systems perform well in simple terrain or on open ground. However, when discrete objects such as trees are present in the scene, correlation-based approaches exhibit inherent difficulties. Some of these difficulties are introduced during the preprocessing stage that attempts to compensate for photometric variations between the cameras. Other difficulties occur during the correlation stage due to occlusion. As a result, object portions appear enlarged, contracted, or missing, as the range data bleeds between the foreground object and the background. This complicates subsequent obstacle detection, representation and modeling. These problems have been addressed by more sophisticated stereo algorithms based on energy minimization and global optimization schemes. Such complex algorithms, however, are computationally demanding and not amenable to real-time implementation. Our solution uses a better preprocessing method, intelligent use of edge cues, and a variation of the traditional shiftable window approach to enhance the stereo correlation at and near depth discontinuities. There is additional computational overhead involved, but we are able to maintain real-time performance. We present details of our new algorithm and several results in complex natural environments.