RGB-D SLAM in Low-Texture Indoor Environments: Complementary Structural Constraints and Resource-Aware Mapping

Low-texture indoor scenes weaken repeatable point correspondences while often retaining geometric information in image boundaries and depth-supported surfaces. We examine the pose-estimation benefits and resource costs of combining these complementary measurements in RGB-D SLAM. The evaluated system integrates point-reprojection, endpoint-to-line, direct-plane, and structural plane-relation residuals using information scales and modality coefficients fixed before evaluation. Multiple observations of the same physical plane from frames selected for map insertion are consolidated into persistent plane instances, while a compact indoor visual vocabulary supports keyframe-level place recognition. On TUM RGB-D fr3/structure_notexture_near, translational ATE RMSE decreased from 0.052350 m for point only (P) to 0.021353 m for point–line (P + L) and 0.017041 m for point–line–plane (P + L + Π, where P denotes point, L line, and Π plane). Median tracking-call latency increased from 16.174 to 50.589 and 92.505 ms, respectively. The P + L + Π ATE RMSE was 10.3–18.9% below the published results of three closely related PLP methods on the same sequence. Across four ICL-NUIM sequences, reconstruction RMS remained within 0.020–0.055 m. The indoor vocabulary reduced storage from 145.3 to 15.7 MB and loading time from 3235 to 255 ms. Across three P + L + Π runs and 221 valid resource samples, median resident set size was 840.5 MiB, peak resident set size remained below 1.08 GiB, median CPU demand was approximately 1.84 logical cores, and swap consumption remained zero. The results quantify the trajectory-accuracy gains and computational costs of complementary geometric measurements in low-texture indoor RGB-D SLAM.