Customizable Perturbation Synthesis for Robust SLAM Benchmarking

• URL: http://arxiv.org/abs/2402.08125v1
• Date: Mon, 12 Feb 2024 23:49:40 GMT
• Title: Customizable Perturbation Synthesis for Robust SLAM Benchmarking
• Authors: Xiaohao Xu, Tianyi Zhang, Sibo Wang, Xiang Li, Yongqi Chen, Ye Li, Bhiksha Raj, Matthew Johnson-Roberson, Xiaonan Huang
• Abstract summary: We propose a novel, customizable pipeline for noisy data synthesis. This pipeline incorporates customizable hardware setups, software components, and perturbed environments. We instantiate the Robust-SLAM benchmark, which includes diverse perturbation types, to evaluate the risk tolerance of existing advanced SLAM models.
• Score: 33.74471840597803
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Robustness is a crucial factor for the successful deployment of robots in unstructured environments, particularly in the domain of Simultaneous Localization and Mapping (SLAM). Simulation-based benchmarks have emerged as a highly scalable approach for robustness evaluation compared to real-world data collection. However, crafting a challenging and controllable noisy world with diverse perturbations remains relatively under-explored. To this end, we propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against various perturbations. This pipeline incorporates customizable hardware setups, software components, and perturbed environments. In particular, we introduce comprehensive perturbation taxonomy along with a perturbation composition toolbox, allowing the transformation of clean simulations into challenging noisy environments. Utilizing the pipeline, we instantiate the Robust-SLAM benchmark, which includes diverse perturbation types, to evaluate the risk tolerance of existing advanced multi-modal SLAM models. Our extensive analysis uncovers the susceptibilities of existing SLAM models to real-world disturbance, despite their demonstrated accuracy in standard benchmarks. Our perturbation synthesis toolbox, SLAM robustness evaluation pipeline, and Robust-SLAM benchmark will be made publicly available at https://github.com/Xiaohao-Xu/SLAM-under-Perturbation/.

Related papers

• Performance evaluation of SLAM-ASR: The Good, the Bad, the Ugly, and the Way Forward [10.914414815406275]
  Recent research has demonstrated that training a linear connector between speech foundation encoders and large language models (LLMs) enables this architecture to achieve strong ASR capabilities. Despite the impressive results, it remains unclear whether these simple approaches are robust enough across different scenarios and speech conditions. We present novel empirical findings that offer insights on how to effectively utilize the SLAM-ASR architecture across a wide range of settings.
  arXiv  Detail & Related papers  (2024-11-06T12:22:04Z)
• SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models [85.67096251281191]
  We present an innovative approach to model fusion called zero-shot Sparse MIxture of Low-rank Experts (SMILE) construction. SMILE allows for the upscaling of source models into an MoE model without extra data or further training. We conduct extensive experiments across diverse scenarios, such as image classification and text generation tasks, using full fine-tuning and LoRA fine-tuning.
  arXiv  Detail & Related papers  (2024-08-19T17:32:15Z)
• From Perfect to Noisy World Simulation: Customizable Embodied Multi-modal Perturbations for SLAM Robustness Benchmarking [32.52171076424419]
  Embodied agents require robust navigation systems to operate in unstructured environments. We propose a novel, customizable pipeline for noisy data synthesis. Our analysis uncovers the susceptibilities of both neural (NeRF) and non-neural SLAM models to disturbances.
  arXiv  Detail & Related papers  (2024-06-24T17:57:05Z)
• Towards Evaluating the Robustness of Visual State Space Models [63.14954591606638]
  Vision State Space Models (VSSMs) have demonstrated remarkable performance in visual perception tasks. However, their robustness under natural and adversarial perturbations remains a critical concern. We present a comprehensive evaluation of VSSMs' robustness under various perturbation scenarios.
  arXiv  Detail & Related papers  (2024-06-13T17:59:44Z)
• Addressing Misspecification in Simulation-based Inference through Data-driven Calibration [43.811367860375825]
  Recent work has demonstrated that model misspecification can harm simulation-based inference's reliability. This work introduces robust posterior estimation (ROPE), a framework that overcomes model misspecification with a small real-world calibration set of ground truth parameter measurements.
  arXiv  Detail & Related papers  (2024-05-14T16:04:39Z)
• $\text{R}^2$-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations [36.74309198908876]
  We present a comprehensive taxonomy of perturbations, and then develop a versatile toolbox for synthesizing and evaluating the effects of composite disturbances. We propose the $textR2$-Agent, an LLM-based agent that simplifies and automates model evaluation via natural language instructions.
  arXiv  Detail & Related papers  (2024-03-07T22:18:12Z)
• INTAGS: Interactive Agent-Guided Simulation [4.04638613278729]
  In many applications involving multi-agent system (MAS), it is imperative to test an experimental (Exp) autonomous agent in a high-fidelity simulator prior to its deployment to production. We propose a metric to distinguish between real and synthetic multi-agent systems, which is evaluated through the live interaction between the Exp and BG agents. We show that using INTAGS to calibrate the simulator can generate more realistic market data compared to the state-of-the-art conditional Wasserstein Generative Adversarial Network approach.
  arXiv  Detail & Related papers  (2023-09-04T19:56:18Z)
• Realistic Noise Synthesis with Diffusion Models [68.48859665320828]
  Deep image denoising models often rely on large amount of training data for the high quality performance. We propose a novel method that synthesizes realistic noise using diffusion models, namely Realistic Noise Synthesize Diffusor (RNSD) RNSD can incorporate guided multiscale content, such as more realistic noise with spatial correlations can be generated at multiple frequencies.
  arXiv  Detail & Related papers  (2023-05-23T12:56:01Z)
• Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation [117.3856882511919]
  We propose the Style-HAllucinated Dual consistEncy learning (SHADE) framework to handle domain shift. Our SHADE yields significant improvement and outperforms state-of-the-art methods by 5.07% and 8.35% on the average mIoU of three real-world datasets.
  arXiv  Detail & Related papers  (2022-04-06T02:49:06Z)
• Pushing the Envelope of Rotation Averaging for Visual SLAM [69.7375052440794]
  We propose a novel optimization backbone for visual SLAM systems. We leverage averaging to improve the accuracy, efficiency and robustness of conventional monocular SLAM systems. Our approach can exhibit up to 10x faster with comparable accuracy against the state-art on public benchmarks.
  arXiv  Detail & Related papers  (2020-11-02T18:02:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.