This paper proposes a new localization method for mobile robots based on received signal strength (RSS) in indoor wireless local area networks (WLANs). In indoor wireless networks, propagation conditions are very difficult to predict due to interference, reflection, and fading effects. As a result, an explicit measurement equation is not available. In this paper, an observation likelihood model is accomplished using kernel density estimation to characterize the dependence of location and RSS. Based on the measured RSS, the robot's location is dynamically estimated using the proposed adaptive local search particle filter (ALSPF), which adopts the covariance adaptation for correcting the system states and updating the motion uncertainty. To deal with low sensor density in large-space environments, we present a strategy based on the strongest signal with minimum variance to choose a subset of detectable access points (APs) for enhancing robot localization and reducing the computational burden. The proposed approaches are verified by realistic low-density WLAN APs to demonstrate the feasibility and suitability. Experimental results indicate that the proposed ALSPF provides approximately 1-m error and significant improvements over particle filtering.