People with severe motor impairment oftentimes rely on unconventional assistive interfaces to complete daily activities. In this work, we developed a holistic shared control framework to help them achieve fast and accurate indoor navigation using low throughput human machine interfaces. Based off our previously developed shared position control method, we added orientation control, action control, and a state machine architecture to create the new framework Novelti 2.0 . The framework provides generic support for various LTIs. We validated the framework using Emotiv EPOC headset and a single-switch interface. A comprehensive comparative study involving 10 human subjects navigating a wheelchair in a real indoor environment demonstrated that our framework could achieve high navigation accuracy about 0.05 m for position and 5° for orientation. Compared to steering control, our method required half the navigation time and half the number of commands in routes longer than 20 m. The NASA TLX survey results also indicated lower subjective workload with our method. Experimental data is provided for download and future evaluation.