# Efficient and Robust Semantic Mapping for Indoor Environments

arXiv: https://arxiv.org/abs/2203.05836 · HTML: https://mlsanity.com/paper/2203.05836/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2022.

## Built on by (most cited first, 5 of 5)

- [Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments](https://mlsanity.com/paper/2207.04526/) (2022)
- [PanopticNDT: Efficient and Robust Panoptic Mapping](https://mlsanity.com/paper/2309.13635/) (2023)
- [Mapping High-level Semantic Regions in Indoor Environments without Object Recognition](https://mlsanity.com/paper/2403.07076/) (2024)
- [RAVEN: Long-Horizon Reasoning & Navigation with a Visuo-Spatio-Temporal Memory](https://mlsanity.com/paper/2606.25206/) (2026)
- [Efficient Prediction of Dense Visual Embeddings via Distillation and RGB-D Transformers](https://mlsanity.com/paper/2601.00359/) (2026)

## Abstract (arXiv)

A key proficiency an autonomous mobile robot must have to perform high-level tasks is a strong understanding of its environment. This involves information about what types of objects are present, where they are, what their spatial extend is, and how they can be reached, i.e., information about free space is also crucial. Semantic maps are a powerful instrument providing such information. However, applying semantic segmentation and building 3D maps with high spatial resolution is challenging given limited resources on mobile robots. In this paper, we incorporate semantic information into efficient occupancy normal distribution transform (NDT) maps to enable real-time semantic mapping on mobile robots. On the publicly available dataset Hypersim, we show that, due to their sub-voxel accuracy, semantic NDT maps are superior to other approaches. We compare them to the recent state-of-the-art approach based on voxels and semantic Bayesian spatial kernel inference~(S-BKI) and to an optimized version of it derived in this paper. The proposed semantic NDT maps can represent semantics to the same level of detail, while mapping is 2.7 to 17.5 times faster. For the same grid resolution, they perform significantly better, while mapping is up to more than 5 times faster. Finally, we prove the real-world applicability of semantic NDT maps with qualitative results in a domestic application.