Confidence-rich grid mapping

• URL: http://arxiv.org/abs/2006.15754v1
• Date: Mon, 29 Jun 2020 00:21:30 GMT
• Title: Confidence-rich grid mapping
• Authors: Ali-akbar Agha-mohammadi, Eric Heiden, Karol Hausman, Gaurav S. Sukhatme
• Abstract summary: Representing the environment is a fundamental task in enabling robots to act autonomously in unknown environments. We present confidence-rich mapping, a new algorithm for spatial grid-based mapping of the 3D environment. We show in real-world experiments that, in addition to achieving maps that are more accurate than traditional methods, the proposed filtering scheme demonstrates a much higher level of consistency between its error and the reported confidence.
• Score: 19.530047371535147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Representing the environment is a fundamental task in enabling robots to act autonomously in unknown environments. In this work, we present confidence-rich mapping (CRM), a new algorithm for spatial grid-based mapping of the 3D environment. CRM augments the occupancy level at each voxel by its confidence value. By explicitly storing and evolving confidence values using the CRM filter, CRM extends traditional grid mapping in three ways: first, it partially maintains the probabilistic dependence among voxels. Second, it relaxes the need for hand-engineering an inverse sensor model and proposes the concept of sensor cause model that can be derived in a principled manner from the forward sensor model. Third, and most importantly, it provides consistent confidence values over the occupancy estimation that can be reliably used in collision risk evaluation and motion planning. CRM runs online and enables mapping environments where voxels might be partially occupied. We demonstrate the performance of the method on various datasets and environments in simulation and on physical systems. We show in real-world experiments that, in addition to achieving maps that are more accurate than traditional methods, the proposed filtering scheme demonstrates a much higher level of consistency between its error and the reported confidence, hence, enabling a more reliable collision risk evaluation for motion planning.

Related papers

• Towards Efficient 3D Object Detection for Vehicle-Infrastructure Collaboration via Risk-Intent Selection [23.186947629238233]
  Risk-intent Selective detection (RiSe) is an interaction-aware framework that shifts the paradigm from identifying visible regions to prioritizing risk-critical ones.<n>RiSe implements a semantic-selective fusion scheme that transmits high-fidelity features only from high-interaction regions.<n>Our method reduces communication volume to 0.71% of full feature sharing while maintaining state-of-the-art detection accuracy.
  arXiv  Detail & Related papers  (2026-01-06T13:25:23Z)
• Predictive Uncertainty for Runtime Assurance of a Real-Time Computer Vision-Based Landing System [32.06417142452719]
  We present a vision-based pipeline for aircraft pose estimation from runway images.<n>Our approach features three key innovations: (i) an efficient, flexible neural architecture based on a spatial Soft Argmax operator for probabilistic keypoint regression, supporting diverse vision backbones with real-time inference; (ii) a principled loss function producing predictive uncertainties, which are evaluated via sharpness and calibration metrics; and (iii) an adaptation of Residual-based Receiver Autonomous Integrity Monitoring (RAIM) enabling runtime detection and rejection of faulty model outputs.
  arXiv  Detail & Related papers  (2025-08-13T11:56:22Z)
• Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators [1.7864593554171284]
  Conformalized Monte Carlo Operator (CMCO) transforms neural operator-based virtual sensing with calibrated, distribution-free prediction intervals.<n>CMCO achieves spatially resolved uncertainty estimates without retraining, ensembling, or custom loss design.<n>This breakthrough offers a general-purpose, plug-and-play UQ solution for neural operators, unlocking real-time, trustworthy inference in digital twins, sensor fusion, and safety-critical monitoring.
  arXiv  Detail & Related papers  (2025-07-15T04:26:40Z)
• DriveMRP: Enhancing Vision-Language Models with Synthetic Motion Data for Motion Risk Prediction [14.010956081539476]
  We introduce a Bird's-Eye View (BEV) based motion simulation method to model risks from three aspects: the ego-vehicle, other vehicles, and the environment.<n>This allows us to synthesize plug-and-play, high-risk motion data suitable for Vision-Language Models.<n>We design a VLM-agnostic motion risk estimation framework, named DriveMRP-Agent.
  arXiv  Detail & Related papers  (2025-06-28T21:28:01Z)
• RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion [17.46462636610847]
  Risk- Driving Environment (RADE) is a simulation framework that generates statistically realistic and risk-adjustable traffic scenes.<n>RADE learns risk-conditioned behaviors directly from data, preserving naturalistic multi-agent interactions with controllable risk levels.<n>We validate RADE on the real-world rounD dataset, demonstrating that it preserves statistical realism across varying risk levels.
  arXiv  Detail & Related papers  (2025-05-06T04:41:20Z)
• RiskNet: Interaction-Aware Risk Forecasting for Autonomous Driving in Long-Tail Scenarios [6.024186631622774]
  RiskNet is an interaction-aware risk forecasting framework for autonomous vehicles. It integrates deterministic risk modeling with probabilistic behavior prediction for comprehensive risk assessment. It supports real-time, scenario-adaptive risk forecasting and demonstrates strong generalization across uncertain driving environments.
  arXiv  Detail & Related papers  (2025-04-22T02:36:54Z)
• Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Model [63.336123527432136]
  We introduce Bench2Drive-R, a generative framework that enables reactive closed-loop evaluation. Unlike existing video generative models for autonomous driving, the proposed designs are tailored for interactive simulation. We compare the generation quality of Bench2Drive-R with existing generative models and achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-12-11T06:35:18Z)
• Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priors [2.773055342671194]
  We introduce a natural adversarial scenario generation solution using naturalistic human driving priors and reinforcement learning techniques. Our findings demonstrate that the proposed model can generate realistic safety-critical test scenarios covering both naturalness and adversariality.
  arXiv  Detail & Related papers  (2024-08-06T13:58:56Z)
• Self-supervised Multi-future Occupancy Forecasting for Autonomous Driving [45.886941596233974]
  LiDAR-generated occupancy grid maps (L-OGMs) offer a robust bird's-eye view for the scene representation. Our proposed framework performs L-OGM prediction in the latent space of a generative architecture. We decode predictions using either a single-step decoder, which provides high-quality predictions in real-time, or a diffusion-based batch decoder.
  arXiv  Detail & Related papers  (2024-07-30T18:37:59Z)
• CNN-based Compressor Mass Flow Estimator in Industrial Aircraft Vapor Cycle System [9.007685592514187]
  In Vapor Cycle Systems, the mass flow sensor plays a key role for different monitoring and control purposes. The conception of a virtual sensor, based on other standard sensors, is a good alternative.
  arXiv  Detail & Related papers  (2024-05-27T08:49:47Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences [118.91584633024907]
  A novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems.
  arXiv  Detail & Related papers  (2023-04-29T00:39:50Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• Incremental learning of LSTM framework for sensor fusion in attitude estimation [2.064612766965483]
  This paper presents a novel method for attitude estimation of an object in 3D space by incremental learning of the Long-Short Term Memory (LSTM) network. Inertial sensors data are fed to the LSTM network which are then updated incrementally to incorporate the dynamic changes in motion occurring in the run time. The proposed framework offers a significant improvement in the results compared to the traditional method, even in the case of a highly dynamic environment.
  arXiv  Detail & Related papers  (2021-08-04T09:03:53Z)
• IntentNet: Learning to Predict Intention from Raw Sensor Data [86.74403297781039]
  In this paper, we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. Our multi-task model achieves better accuracy than the respective separate modules while saving computation, which is critical to reducing reaction time in self-driving applications.
  arXiv  Detail & Related papers  (2021-01-20T00:31:52Z)
• Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
  We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control. A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
  arXiv  Detail & Related papers  (2020-09-12T02:02:52Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)

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.