To solve the map construction problem of the quadruped robot for natural gas pipeline inspection, an ISCSO-FastSLAM algorithm optimized by the improved sand cat swarm algorithm is proposed. Firstly, the Cauchy variation strategy is introduced to improve the ability of the sand cat swarm algorithm to jump out of the local optimum and accelerate the convergence speed, and the adaptive genetic parameters are added to improve the stability of the sand cat swarm algorithm. Then, the predicted particle set of the FastSLAM algorithm is updated by improving the optimal solution of the position prediction output of the sand cat swarm algorithm to improve the estimation accuracy. Meanwhile, the low weight particle optimization strategy is used to replace the original resampling step in particle filtering to ensure the diversity of particles. Then, different simulation environments are constructed to compare the different algorithms, and the simulation results show that the ISCSO-FastSLAM algorithm constructs the map more accurately than the WOA-FastSLAM algorithm, and the estimation errors of the robot position and the environmental signposts are reduced by 17.1% and 23.3%, respectively, under the simulation environment of 20 m×20 m. Finally, the quadruped robot is used to conduct map construction experiments in a residential area of 60 m×100 m, and the experimental results show that, compared with the FastSLAM algorithm and the WOA-FastSLAM algorithm, the ISCSO-FastSLAM algorithm is able to construct a more accurate map of the residential area inspection, and the estimation errors of the key inspection locations such as valve wells and regulator boxes are reduced by 16.2% and 6.0%, respectively.