# Evaluating the Impact of Semantic Segmentation and Pose Estimation on Dense Semantic SLAM Source: [Evaluating the Impact of Semantic Segmentation and Pose Estimation on Dense Semantic SLAM]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:50:06.694Z Authors: Suman Raj Bista, David Hall, Ben Talbot, Haoyang Zhang, Feras Dayoub, Niko S\\"underhauf Published: 2021-09-16T06:46:31.000Z ## Abstract Recent Semantic SLAM methods combine classical geometry-based estimation with deep learning-based object detection or semantic segmentation. In this paper we evaluate the quality of semantic maps generated by state-of-the-art class- and instance-aware dense semantic SLAM algorithms whose codes are publicly available and explore the impacts both semantic segmentation and pose estimation have on the quality of semantic maps. We obtain these results by providing algorithms with ground-truth pose and/or semantic segmentation data available from simulated environments. We establish that semantic segmentation is the largest source of error through our experiments, dropping mAP and OMQ performance by up to 74.3% and 71.3% respectively. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()