Autonomous mobile robots need maps for effective, safe navigation, and SLAM in general is still an unsolved problem. Nonetheless, certain combinations of environmental characteristics and sensors admit tractable solutions. In particular, detection and tracking of linear features such as line segments (2D) or planar facets (3D) has been proven robust in many man-made environments. However, these types of features produce rank-deficient constraints, which create challenges for graph-based SLAM optimizers. We present techniques for using rank-deficient features and constraints more robustly by analyzing the approximate null-space of the constraints for each node in the factor graph representing the trajectory. We also extend auxiliary methods for correspondence calculations and map update routines, the combination of which yields state-of-the-art performance for a rank-deficient SLAM system. We present results from quantitative experiments comparing memory use, compute load, accuracy, and robustness for several ablation tests on real and simulated data.