Conventional SLAM methods which work very well in typical above-water situations, are based on detecting keypoints that are tracked between images, from which egomotion and the 3D structure of the scene are estimated. However, in underwater environments with marine snow -small particles of organic matter which are carried by ocean currents throughout the water column -keypoint detectors are prone to detect the marine snow particles. As the vast majority of SLAM front ends are sensitive against outliers, and the marine snow acts as severe "motion noise", failure of the regular egomotion and 3D structure estimation is expected. For this reason, we investigate the structure and appearance of marine snow and developed two schemes which classify keypoints into "marine snow" or "clean" based on either the image patches obtained from usual keypoint detectors or the descriptors computed from these patches. This way the subsequent SLAM pipeline is protected against 'false' keypoints. We quantitatively evaluate the performance of our marine snow classifier on both real underwater video scenes as well as on simulated underwater footage that contains marine snow. These simulated image sequences have been created by extracting real marine snow elements from real underwater footage, and subsequently overlaying these on "clean" underwater videos. Qualitative evaluation is also done on a night-time road sequence with snowfall to demonstrate applicability in other areas of autonomy. We furthermore evaluate the performance and the effect of marine snow detection & suppression by integrating the snow suppression module in a full SLAM pipeline based on the pySLAM system.