A probabilistic algorithm is proposed for the problem of simultaneous robot localization and people-tracking (SLAP) using single onboard sensor in situations with sensor noise and global uncertainties over the observer’s pose. By the decomposition of the joint distribution according to the Rao-Blackwell theorem, posteriors of the robot pose are sequentially estimated over time by a smoothed laser perception model and an improved resampling scheme with evolution strategies; the conditional distribution of the person’s position is estimated using unscented Kalman filter (UKF) to deal with the nonlinear dynamic of human motion. Experiments conducted in a real indoor service robot scenario validate the favorable performance of the positional accuracy as well as the improved computational efficiency. A probabilistic algorithm is proposed for the problem of simultaneous robot localization and people-tracking (SLAP) using single onboard sensor in situations with sensor noise and global uncertainties over the observer’s pose. By the decomposition of the joint distribution according to the Rao-Blackwell theorem , posteriors of the robot pose are sequentially estimated over time by a smoothed laser perception model and an improved resampling scheme with evolution strategies; the conditional distribution of the person’s position is estimated using unscented Kalman filter (UKF) to deal with the nonlinear dynamic of human motion. Experiments conducted in a real indoor service robot scenario validate the favorable performance of the positional accuracy as well as the improved computational efficiency.