Centerline-Based Navigation Formulation

Updated 10 February 2026

Centerline-based navigation formulation is an approach that defines navigation problems using a representative 1D curve extracted from domain-specific structures.
It dramatically reduces high-dimensional search spaces into a simpler 1D state space, enabling faster convergence and more robust inference.
The method integrates techniques like centerline extraction, deep learning, and graph-based planning to unify perception and control in various applications.

A centerline-based navigation formulation is an approach in which the solution to a navigation, localization, or path planning problem is defined with respect to a representative 1D curve—the centerline—extracted from a domain-specific structure (e.g., anatomical chambers, roads, rib bones, or lanes). The approach leverages the reduced dimensionality and geometric semantics of centerlines to improve inference efficiency, robustness, and accuracy. This paradigm underpins state-of-the-art methods in clinical navigation, autonomous driving, and robotic path planning, unifying perception and control via precise centerline representations. Key methodological aspects include centerline extraction (from images or volumetric data), centerline-based state and action space construction, deep learning or reinforcement learning over 1D domains, and the transformation of mask-based or parametric representations into actionable navigation graphs.

1. Centerline Extraction and Representation

Centerline extraction begins with data-specific preprocessing—commonly semantic segmentation, thresholding, or connected-component analysis, depending on the domain.

In medical navigation, such as left atrial appendage (LAA) or rib centerline analysis, the process starts with binarization or geodesic-seeded region growing, followed by a Euclidean distance transform. High-distance ridges in the foreground mask correspond to maximal inscribed spheres, guiding greedy tracking for centerline construction (Al et al., 2019, Jin et al., 2022).
For road/lane navigation, after camera image segmentation and an inverse perspective (IPM/Bird’s Eye View) projection, lateral boundaries or lane markings are fit with splines, and a centerline is generated as a geometric mean or via a polynomial fit (Cudrano et al., 2020, Chen, 1 Dec 2025).
Skeletonization algorithms (TEASAR or L1-medial) extract 1D pixel/voxel or point cloud chains from binary volumes or BEV mask predictions, smoothed and resampled for uniformity (Jin et al., 2022, Kalfaoglu et al., 2023, Kalfaoglu et al., 2024).

This centerline is typically represented as a parametric curve $c(s)$ or a discrete set of points, annotated by associated features (e.g., centerline-to-surface depth, extracted radii, or tangent direction).

2. Centerline-Constrained State Spaces and Dimensionality Reduction

One of the primary advantages of the centerline-based formulation is a dramatic reduction in search space dimensionality:

Instead of operating over a full 3D volume ( $10^7$ + voxels in typical medical applications), navigation or localization reduces to movement along a 1D chain (e.g., $T \sim 300$ nodes).
In road and lane navigation, the intrinsic centerline frame converts vehicle pose tracking and planning into operations directly along the curve (curvilinear abscissa $s$ , tangent angle $\vartheta(s)$ ), minimizing spatial context switching and instability (Cudrano et al., 2020, Chen, 1 Dec 2025).

This simplified topology allows navigation algorithms—be they RL agents, controllers, or shortest-path planners—to converge orders of magnitude faster, collect informative overlapping states, and avoid sparse reward or gradient issues common in high-dimensional settings (Al et al., 2019, Jin et al., 2022).

3. Centerline-Based Deep Learning and Reinforcement Learning

The centrality of the centerline is exploited in both supervised and reinforcement learning (RL) contexts:

In medical RL, navigation is posed as a Markov decision process (MDP) on the centerline index. Agents observe a local window of features (e.g., the 1D depth signature $d_{i-k}$ to $d_{i+k}$ ) and execute simple actions ("forward", "backward") to locate anatomical targets, with reward shaped by proximity and progress (Al et al., 2019).
In TopoMask/TopoMaskV2 for road topology, a set of transformer queries predicts instance masks (centerline regions) and direction labels. Each mask undergoes skeletonization, producing thin, ordered centerlines that are post-processed to form topological graphs for navigation and decision-making (Kalfaoglu et al., 2023, Kalfaoglu et al., 2024).
In lane-based robotics, polynomial curves are fit to centerline points extracted from projected camera images, producing parametric models suitable for analytical tracking/controller synthesis (Chen, 1 Dec 2025).

Model architectures commonly exploit convolutional, recurrent, or transformer-based components, and each is optimized for the reduced 1D input.

With the centerline extracted, navigation tasks utilize the centerline in several ways:

Graph-based path planning: The skeletonized or discretized centerline forms a directed or undirected graph. Nodes correspond to polyline or anatomical waypoints; edges are weighted by geometric cost (arc length, risk metrics, or radii for collision avoidance). Shortest-path algorithms (Dijkstra, A*) route agents or instruments along the graph under application-specific constraints (Jin et al., 2022, Kalfaoglu et al., 2023).
Control using geometric features: Lateral and heading errors are measured with respect to the centerline. Controllers (PID, Stanley, pure pursuit, nonlinear MPC) or Lyapunov-based stabilizers directly use the tangent angle and curvature profiles of the centerline for precise, stable tracking (Cudrano et al., 2020, Chen, 1 Dec 2025).
RL-based fine localization: For tasks where end-to-end regression suffers due to anatomical or environmental noise, RL on the 1D centerline, with locally processed feature vectors, enables reliable target localization (e.g., LAA orifice with 2.55 mm mean error versus 8.9 mm for rule-based methods) (Al et al., 2019).

5. Instance-Mask and Transformer-Based Approaches

Mask-based and transformer architectures further generalize centerline-based navigation, especially in road topology:

Methods such as TopoMask and TopoMaskV2 employ transformers where object queries produce both dense instance masks and discrete direction labels (quad-direction) for each centerline in a BEV grid.
Post-processing steps include thresholding, skeletonization, polynomial fitting, and direction-aware sorting to obtain ordered, actionable 3D point sets (Kalfaoglu et al., 2023, Kalfaoglu et al., 2024).
Fusion with Bézier curves, multi-height-bin BEV feature stacking, and quad-direction label representation add robustness and improve topological graph quality for real-time navigation and dynamic replanning.
These pipelines set state-of-the-art benchmarks on datasets such as OpenLane-V2, improving the OpenLane Score (OLS) and Chamfer/Frechet metrics for centerline and topology accuracy (Kalfaoglu et al., 2024).

6. Evaluation Metrics and Empirical Results

Domain-specific evaluation of centerline-based navigation relies on established geometric and topological accuracy metrics:

Average distance error, Chamfer distance, Hausdorff distance for rib centerline extraction and other anatomical settings (Jin et al., 2022).
Frechet and Chamfer distances (after curve sampling) for predicted versus ground-truth centerline in road topology (Kalfaoglu et al., 2023, Kalfaoglu et al., 2024).
Task-specific outcome measures such as orifice-plane orientation error (9.9° for RL centerline vs 18.2° rule-based), area difference (72 mm² vs >140 mm²), and speedup (8 seconds vs ~150 seconds) in LAA orifice localization (Al et al., 2019).
Controller and planner accuracy: Precise lateral displacement and heading error feedback from centerlines lead to stable, convergent robotic control and trajectory fidelity in practice (Chen, 1 Dec 2025).

Key empirical findings demonstrate both quantitative gains over prior methods and that centerline-based errors approach or even match expert inter-observer variability in several domains.

7. Integration, Limitations, and Application Scope

Centerline-based navigation formulations are now core to a range of systems in clinical, robotic, and automotive environments:

In robotics, integrated pipelines (e.g., YOLOP perception + Lyapunov-based control) operate fully closed-loop under real-time constraints, with cubic polynomial centerline models and embedded deployment (Chen, 1 Dec 2025).
In medicine, reinforcement learning operating over a centerline drastically reduces annotation burden and improves automation in 3D volumetric navigation (Al et al., 2019, Jin et al., 2022).
In road navigation, transformer-based centerline extraction and graph-building pipelines enable robust operation even under missing or ambiguous lane marking, dynamic scene changes, or partial occlusion (Kalfaoglu et al., 2023, Kalfaoglu et al., 2024).
Generalizations include multi-height quantization, direction-aware fusion, and joint instance–parametric formulations. Centerline-based methods are robust to domain shifts, noise, and operate effectively even with sparse or disconnected ground-truth.

Recognized limitations include reliance on initial segmentation fidelity, skeletonization artifacts, and downstream sensitivity to curve fitting and real-time graph update lags—each partially mitigated in recent transformer and mask-based designs.

References:

(Al et al., 2019) Centerline Depth World Reinforcement Learning-based Left Atrial Appendage Orifice Localization
(Jin et al., 2022) RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction
(Cudrano et al., 2020) Advances in centerline estimation for autonomous lateral control
(Kalfaoglu et al., 2023) TopoMask: Instance-Mask-Based Formulation for the Road Topology Problem via Transformer-Based Architecture
(Kalfaoglu et al., 2024) TopoMaskV2: Enhanced Instance-Mask-Based Formulation for the Road Topology Problem
(Chen, 1 Dec 2025) Integrated YOLOP Perception and Lyapunov-based Control for Autonomous Mobile Robot Navigation on Track