Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping

• URL: http://arxiv.org/abs/2002.01921v1
• Date: Wed, 5 Feb 2020 18:54:07 GMT
• Title: Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping
• Abstract summary: This paper focuses on real-time occupancy mapping and collision checking onboard an autonomous robot navigating in an unknown environment. We propose a new map representation, in which occupied and free space are separated by the decision boundary of a kernel perceptron classifier. We develop an online training algorithm that maintains a very sparse set of support vectors to represent obstacle boundaries in configuration space.
• Score: 19.169233494235314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper focuses on real-time occupancy mapping and collision checking onboard an autonomous robot navigating in an unknown environment. We propose a new map representation, in which occupied and free space are separated by the decision boundary of a kernel perceptron classifier. We develop an online training algorithm that maintains a very sparse set of support vectors to represent obstacle boundaries in configuration space. We also derive conditions that allow complete (without sampling) collision-checking for piecewise-linear and piecewise-polynomial robot trajectories. We demonstrate the effectiveness of our mapping and collision checking algorithms for autonomous navigation of an Ackermann-drive robot in unknown environments.

Related papers

• Factorized Spatio-Temporal Convolutions for Human Pose Estimation from Planar Lidar [43.576051446645856]
  Localizing nearby humans and estimating their facing direction are key capabilities for safe navigation and socially aware human-robot interaction.<n>We address omnidirectional human detection and relative 2D pose estimation from planar LiDAR sequences with a lightweight network based on Space-Time Blocks.<n>Our network processes 360 LiDAR sequences to output per-ray human presence, distance, and relative orientation.
  arXiv  Detail & Related papers  (2026-07-23T13:31:54Z)
• AutoPath: Learning Transferable Goal-Conditioned Stochastic Path Prior for Safe Navigation Without Human Demonstrations [103.61864552260316]
  Real-time navigation in dynamic environments requires collision-free and dynamically feasible motion under limited perception.<n>We formulate navigation as learning a transferable goal-aligned path prior that models a reusable distribution over goal-aligned geometry-consistent local paths conditioned on local observations.<n>We develop a structured prior learning framework that parameterizes local paths using a geometry-aware polar action manifold and incorporates risk-sensitive utility shaping with multi-goal distributional rollouts for stable and safety-aware planning.
  arXiv  Detail & Related papers  (2026-07-13T16:00:41Z)
• Reactive Robot-Centric Safety for Autonomous Navigation in Constrained and Dynamic Environments [14.321190258774358]
  We present a real-time control architecture that integrates a 3D LIDAR perception-based composite control barrier function.<n>We validate the system through multiple field experiments in underground environments.
  arXiv  Detail & Related papers  (2026-05-15T09:42:13Z)
• The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy [13.105188128582567]
  We introduce and open-source the Unified Autonomy Stack, a system-level solution that enables resilient autonomy across aerial and ground robot morphologies.<n>The architecture centers on three modules -- multi-modal perception, multi-behavior planning, and multi-layered safe navigation.<n>The stack has been field-tested and validated on both aerial (rotorcraft) and ground (legged) robots.
  arXiv  Detail & Related papers  (2026-05-12T20:39:35Z)
• QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving [33.609780917199394]
  Self-driving vehicles must understand its environment to determine appropriate action. Traditional systems rely on object detection to find agents in the scene. We present a unified, interpretable, and efficient autonomy framework that moves away from cascading modules that first perceive occupancy relevant-temporal autonomy.
  arXiv  Detail & Related papers  (2024-04-01T21:11:43Z)
• NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning [67.53972459080437]
  This paper presents NeuPAN: a real-time, highly-accurate, robot-agnostic, and environment-invariant robot navigation solution. Leveraging a tightly-coupled perception-locomotion framework, NeuPAN has two key innovations compared to existing approaches. We evaluate NeuPAN on car-like robot, wheel-legged robot, and passenger autonomous vehicle, in both simulated and real-world environments.
  arXiv  Detail & Related papers  (2024-03-11T15:44:38Z)
• Construction of Object Boundaries for the Autopilotof a Surface Robot from Satellite Imagesusing Computer Vision Methods [101.18253437732933]
  A method for detecting water objects on satellite maps is proposed. An algorithm for calculating the GPS coordinates of the contours is created. The proposed algorithm allows saving the result in a format suitable for the surface robot autopilot module.
  arXiv  Detail & Related papers  (2022-12-05T12:07:40Z)
• Polyline Based Generative Navigable Space Segmentation for Autonomous Visual Navigation [57.3062528453841]
  We propose a representation-learning-based framework to enable robots to learn the navigable space segmentation in an unsupervised manner. We show that the proposed PSV-Nets can learn the visual navigable space with high accuracy, even without any single label.
  arXiv  Detail & Related papers  (2021-10-29T19:50:48Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• XAI-N: Sensor-based Robot Navigation using Expert Policies and Decision Trees [55.9643422180256]
  We present a novel sensor-based learning navigation algorithm to compute a collision-free trajectory for a robot in dense and dynamic environments. Our approach uses deep reinforcement learning-based expert policy that is trained using a sim2real paradigm. We highlight the benefits of our algorithm in simulated environments and navigating a Clearpath Jackal robot among moving pedestrians.
  arXiv  Detail & Related papers  (2021-04-22T01:33:10Z)
• Inverse reinforcement learning for autonomous navigation via differentiable semantic mapping and planning [20.66819092398541]
  This paper focuses on inverse reinforcement learning for autonomous navigation using distance and semantic category observations. We develop a map encoder, that infers semantic category probabilities from the observation sequence, and a cost encoder, defined as a deep neural network over the semantic features. We show that our approach learns to follow traffic rules in the autonomous driving CARLA simulator by relying on semantic observations of buildings, sidewalks, and road lanes.
  arXiv  Detail & Related papers  (2021-01-01T07:41:08Z)
• High-Speed Robot Navigation using Predicted Occupancy Maps [0.0]
  We study algorithmic approaches that allow the robot to predict spaces extending beyond the sensor horizon for robust planning at high speeds. We accomplish this using a generative neural network trained from real-world data without requiring human annotated labels. We extend our existing control algorithms to support leveraging the predicted spaces to improve collision-free planning and navigation at high speeds.
  arXiv  Detail & Related papers  (2020-12-22T16:25:12Z)
• 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.