To address the challenges of limited space in fully mechanized excavation faces and difficulty achieving long-term high-precision roadheader pose monitoring with single sensors, a dual filtering fusion method using heterogeneous sensors is proposed. First, a laser vision subsystem employing an improved adaptive Canny operator identifies and analyzes laser spot geometry to obtain lateral offset, vertical offset, and three-axis attitude. Second, an ultra-wideband (UWB) subsystem based on an improved weighted Chan-Taylor hybrid method effectively compensates for non-line-of-sight errors and reduce dependency on initial values in traditional UWB positioning. A roadheader body attitude calculation compensation model is also established to reduce the impact of environmental signal reflections on positioning accuracy. Building upon these subsystems, a complete dual filtering combined pose fusion framework is constructed. An improved adaptive extended Kalman filter algorithm is applied to perform primary filtering and noise reduction on the pose outputs from both the laser vision and UWB subsystems. Subsequently, an adaptive weighting algorithm conducts secondary fusion filtering on the redundant pose parameters, compensating for UWB calculation errors while overcoming laser vision data loss from temporary target loss, thereby achieving complementary advantages of multi-source sensors. Finally, based on a scaled prototype of the EBZ200 roadheader, an experimental platform for multi-source sensor combined pose perception is established. Results demonstrate that the multi-source heterogeneous sensor filtering fusion pose perception system achieves a machine body position detection error of less than 13 mm and an attitude detection errors of less than 0.8°, providing an effective technical solution for continuous precise positioning of roadheader in fully mechanized excavation processes.