# Evidential Semantic Mapping in Off-road Environments with Uncertainty-aware Bayesian Kernel Inference

arXiv: https://arxiv.org/abs/2403.14138 · HTML: https://mlsanity.com/paper/2403.14138/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 45 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 27 of 27)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users](https://mlsanity.com/paper/2007.06823/) (2020)
- [Trusted Multi-View Classification with Dynamic Evidential Fusion](https://mlsanity.com/paper/2204.11423/) (2022)
- [RELLIS-3D Dataset: Data, Benchmarks and Analysis](https://mlsanity.com/paper/2011.12954/) (2020)
- [Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping](https://mlsanity.com/paper/1909.04631/) (2019)
- [Learn to Accumulate Evidence from All Training Samples: Theory and Practice](https://mlsanity.com/paper/2306.11113/) (2023)
- [Learning Off-Road Terrain Traversability with Self-Supervisions Only](https://mlsanity.com/paper/2305.18896/) (2023)
- [EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy](https://mlsanity.com/paper/2311.06234/) (2023)
- [DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks](https://mlsanity.com/paper/1703.03098/) (2017)
- [Fast Road Segmentation via Uncertainty-aware Symmetric Network](https://mlsanity.com/paper/2203.04537/) (2022)
- [Uncertainty-aware Panoptic Segmentation](https://mlsanity.com/paper/2206.14554/) (2022)
- [Recurrent-OctoMap: Learning State-based Map Refinement for Long-Term Semantic Mapping with 3D-Lidar Data](https://mlsanity.com/paper/1807.00925/) (2018)
- [Semantic OcTree Mapping and Shannon Mutual Information Computation for Robot Exploration](https://mlsanity.com/paper/2112.04063/) (2021)
- [Reliability in Semantic Segmentation: Are We on the Right Track?](https://mlsanity.com/paper/2303.11298/) (2023)
- [Semantic 3D Occupancy Mapping through Efficient High Order CRFs](https://mlsanity.com/paper/1707.07388/) (2017)
- [Probabilistic Semantic Mapping for Urban Autonomous Driving Applications](https://mlsanity.com/paper/2006.04894/) (2020)
- [The continuous categorical: a novel simplex-valued exponential family](https://mlsanity.com/paper/2002.08563/) (2020)
- [ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty](https://mlsanity.com/paper/2310.16020/) (2023)
- [Robust Fusion for Bayesian Semantic Mapping](https://mlsanity.com/paper/2303.07836/) (2023)
- [Bridging Active Exploration and Uncertainty-Aware Deployment Using Probabilistic Ensemble Neural Network Dynamics](https://mlsanity.com/paper/2305.12240/) (2023)
- [On the Overconfidence Problem in Semantic 3D Mapping](https://mlsanity.com/paper/2311.10018/) (2023)
- [Memory-based Semantic Segmentation for Off-road Unstructured Natural Environments](https://mlsanity.com/paper/2108.05635/) (2021)
- [GMMap: Memory-Efficient Continuous Occupancy Map Using Gaussian Mixture Model](https://mlsanity.com/paper/2306.03740/) (2023)

## Built on by (most cited first, 6 of 6)

- [Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting](https://mlsanity.com/paper/2411.02547/) (2024)
- [EvidMTL: Evidential Multi-Task Learning for Uncertainty-Aware Semantic Surface Mapping from Monocular RGB Images](https://mlsanity.com/paper/2503.04441/) (2025)
- [ZeST: A VLM-based Zero-Shot Traversability Navigation for Unknown Environments](https://mlsanity.com/paper/2508.19131/) (2025)
- [Uncertainty-aware Semantic Mapping in Off-road Environments with Dempster-Shafer Theory of Evidence](https://mlsanity.com/paper/2405.06265/) (2024)
- [Bi-level Meta-Policy Control for Dynamic Uncertainty Calibration in Evidential Deep Learning](https://mlsanity.com/paper/2510.08938/) (2025)
- [VoxelFix: Post-Hoc Semantic Correction of Completed 3D Voxel Maps](https://mlsanity.com/paper/2609.05114/) (2026)

## Abstract (arXiv)

Robotic mapping with Bayesian Kernel Inference (BKI) has shown promise in creating semantic maps by effectively leveraging local spatial information. However, existing semantic mapping methods face challenges in constructing reliable maps in unstructured outdoor scenarios due to unreliable semantic predictions. To address this issue, we propose an evidential semantic mapping, which can enhance reliability in perceptually challenging off-road environments. We integrate Evidential Deep Learning into the semantic segmentation network to obtain the uncertainty estimate of semantic prediction. Subsequently, this semantic uncertainty is incorporated into an uncertainty-aware BKI, tailored to prioritize more confident semantic predictions when accumulating semantic information. By adaptively handling semantic uncertainties, the proposed framework constructs robust representations of the surroundings even in previously unseen environments. Comprehensive experiments across various off-road datasets demonstrate that our framework enhances accuracy and robustness, consistently outperforming existing methods in scenes with high perceptual uncertainties.