Dynamic Adaption of Noise Covariance for Accurate Indoor Localization of Mobile Robots in Non-Line-of-Sight Environments

The estimation of robot pose in an indoor and unknown environment is a challenging problem. Traditional methods using wheel odometry and inertial measurement unit (IMU) are inaccurate due to wheel slippage and drift related issues. Ultra-wide-band (UWB) technology fused with extended Kalman filter (EKF) approach provides relatively accurate ranging and localization in a line-of-sight (LOS) scenario. However, the presence of physical obstacles {such as, walls, doors etc. called as non-line-of-sight (NLOS)} in an indoor environment pose additional challenges which are difficult to address using UWB alone. Identification of LOS/NLOS information can greatly benefit many location-related applications. To this end, an algorithm based on variance measurement technique of distance estimates along with power envelope of the received signal is proposed for NLOS identification. Further, adaptive adjustment of sensor noise covariance approach is devised to mitigate the NLOS effect. The proposed methodology is computationally light and is thoroughly tested. The results demonstrate that the proposed method achieves ~2X improvement in accuracy compared to existing approach.