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Introduction: This article describes an evaluation of MagNav, a speech-based, infrastructure-free indoor navigation system. The research was conducted in the Mall of America, the largest shopping mall in the United States, to empirically investigate the impact of memory load on route-guidance performance. Method: Twelve participants who are blind and 12 age-matched sighted controls participated in the study. Comparisons are made for route-guidance performance between use of updated, real-time route instructions (system-aided condition) and a system-unaided (memory-based condition) where the same instructions were only provided in advance of route travel. The sighted controls (who navigated under typical visual perception but used the system for route guidance) represent a best case comparison benchmark with the blind participants who used the system. Results: Results across all three test measures provide compelling behavioral evidence that blind navigators receiving real-time verbal information from the MagNav system performed route travel faster (navigation time), more accurately (fewer errors in reaching the destination), and more confidently (fewer requests for bystander assistance) compared to conditions where the same route information was only available to them in advance of travel. In addition, no statistically reliable differences were observed for any measure in the system-aided conditions between the blind and sighted participants. Posttest survey results corroborate the empirical findings, further supporting the efficacy of the MagNav system. Discussion: This research provides compelling quantitative and qualitative evidence showing the utility of an infrastructure-free, low-memory demand navigation system for supporting route guidance through complex indoor environments and supports the theory that functionally equivalent navigation performance is possible when access to real-time environmental information is available, irrespective of visual status. Implications for designers and practitioners: Findings provide insight for the importance of developers of accessible navigation systems to employ interfaces that minimize memory demands.