In Global Positioning System (GPS) challenged environments such as urban canyons, GPS signals suffer from signal blockage, non-line-of-sight (NLOS) reflections and multipath errors. These factors degrade navigation solutions and hinder fault detection capabilities. The signal errors are typically the result of a structured environment, which is rich with features detectable by visual and laser-based sensor modalities. These features provide a complementary set of measurements, which can be used to improve navigation solutions. In this work, we aim to leverage these measurements to improve both the integrity of the navigation solution and the fault detection and isolation (FDI) capabilities for the combined measurement vector. The proposed algorithm combines GPS pseudoranges with LiDAR odometry to update an Unscented Kalman Filter (UKF) sensor fusion navigation solution. The estimated state generates the expected measurements, which are compared to the received measurements in a Receiver Autonomous Integrity Monitoring (RAIM) based FDI framework. LiDAR odometry covariances, used in the UKF, are scaled for integration with GPS pseudoranges in RAIM-based FDI to detect and isolate faults in the combined measurement vector. To validate our proposed architecture, experiments are conducted using real world data collected on the University of Illinois at Urbana-Champaign campus. It is shown that our proposed algorithm successfully detects and isolates pseudorange faults without GPS pseudorange redundancy. Additionally, it is demonstrated that our proposed architecture improves the integrity of the solutions and the reliability of the FDI test when compared to a GPS-only RAIM implementation.