To　address　the　significant　challenges　posed　by　low-texture　and　dynamic　scenes　in　building　environments　for　robotic　localization,　a　multi-source　data　fusion　based　localization　method　tailored　for　structured　building　scenarios　is　proposed.　By　judiciously　incorporating　multi-primitive　visual　features,　Manhattan　World　Hypothesis　constraints,　and　IMU　(inertial　measurement　unit)　pre-integration　constraints,　the　proposed　localization　system　effectively　tackles　issues　such　as　sparse　features　and　dynamic　personnel　interference　encountered　by　intelligent　bed-and-chair　robots　during　operation.　Overall,　a　parallel　strategy　combining　high-frequency　front-end　pose　estimation　and　low-frequency　backend　optimization　is　adopted　to　enhance　system　real-time　performance.　The　front-end　odometry　provides　relatively　accurate　pose　estimates　within　short　periods,　which　are　further　refined　by　backend　optimization　using　the　Manhattan　World　Hypothesis.　Additionally,　a　multi-camera　joint　localization　strategy　is　introduced　innovatively　to　address　the　limitations　of　the　Manhattan　World　Hypothesis　in　pose　estimation　applications.　Two　cameras　with　different　orientations　are　utilized,　one　to　capture　rich　texture　features　on　the　ground　and　the　other　to　identify　planes　in　the　upper　vertical　space　that　satisfy　the　Manhattan　World　Hypothesis,　which　mitigates　the　impact　of　non-Manhattan　objects　on　localization.　Experimental　results　demonstrate　that,　compared　to　the　existing　methods,　the　proposed　method　significantly　improves　the　localization　accuracy　and　robustness　while　ensuring　real-time　performance,　providing　robust　support　for　the　application　of　intelligent　mobile　bed-and-chair　robots　in　building　environments.　©　2025　Chinese　Academy　of　Sciences.　All　rights　reserved.