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SGE: Semantically-Guided Exploration for Unstructured Environments via Image-Space Waypoint Sampling

Christopher Tatsch, Yu Gu

arXiv:2608.29315v1cs.ROcs.AI

TL;DR

SGE targets the limited semantic awareness of geometry-focused autonomous exploration, especially where terrain or task-relevant visual cues matter. It inserts pixel-level semantics into image-space waypoint sampling and receding-horizon route selection, with mechanisms for failures and backtracking. Across simulations and field deployments, it achieves comparable volumetric coverage while supporting semantic task biasing.

  • Problem

    Geometry-only exploration may not exploit semantic distinctions such as terrain type, signage, or task-relevant landmarks.

  • Method

    SGE samples waypoints from semantic image inputs, orders them with receding-horizon TSP optimization, and uses taboo regions and graph-based relocation for navigation uncertainty.

  • Results

    SGE achieved reliable real-time exploration with volumetric coverage comparable to geometric planners while enabling task-conditioned behavior across simulation and real-world deployments.

  • Takeaways & Limitations

    SGE provides semantic task biasing in addition to geometric exploration, including directing sampled waypoints away from regions classified as mud and water.

Abstract

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This work introduces Semantically-Guided Exploration (SGE), a modular exploration framework for ground vehicles that integrates pixel-level semantic segmentation into sampling-based waypoint selection and receding-horizon route optimization. Unlike conventional geometric exploration methods, SGE evaluates candidate exploration goals directly in the image space using a semantic-aware utility function that accounts for terrain traversability, obstacle proximity, objects of interest, and depth-based exploration reward. Sampled waypoints are projected into 3D and ordered through a real-time Traveling Salesman Problem (TSP) formulation, enabling receding-horizon goal selection. To address real-world navigation uncertainty, the framework introduces mechanisms, including temporary taboo regions to handle navigation failures and a graph-based relocation strategy for efficient backtracking across explored areas. We evaluate SGE in standardized simulation benchmarks against state-of-the-art exploration planners and demonstrate competitive performance in volumetric coverage, while enabling semantic task biasing that cannot be achieved by purely geometric methods. The framework is further validated through real-world experiments using multiple robotic platforms in indoor campus buildings and in limestone and coal mines. Results show consistent performance and adaptability across platforms and domains.

I. INTRODUCTION

SGE addresses the limits of geometry-only exploration by injecting pixel-level semantic reasoning into waypoint generation. It combines semantic waypoint sampling with real-time route management and validates the framework across simulation and diverse robotic deployments.

  • Autonomous exploration is essential in hazardous or disconnected environments where teleoperation is infeasible.
  • Most existing exploration methods rely mainly on geometric information, while onboard semantic cues can provide richer guidance for deciding where to explore.
  • SGE integrates pixel-level semantic perception directly into sampling-based waypoint selection while preserving real-time operation and task-adaptive behavior.
  • Its utility-driven image-space sampling accounts for terrain traversability, obstacle proximity, task-relevant objects, and depth-based exploration incentives without dense semantic 3D maps.
  • A receding-horizon TSP manager, temporary taboo regions, and graph-based relocation address route optimization, navigation failures, and backtracking.
  • SGE was validated with Rhino and Husky ground vehicles in campus buildings, limestone mines, and coal mines.

II. Related Work

Prior exploration methods are organized around information gain, frontiers, sampling, or hybrids, generally using geometric representations. Semantic approaches either fuse semantics into maps or apply it after geometric candidate generation, motivating SGE’s earlier image-space integration.

  • Exploration methods broadly comprise information-based, frontier-based, sampling-based, and hybrid planners.
  • Information-based methods maximize expected information gain, while frontier-based methods target boundaries between known and unknown space.
  • Sampling-based planners evaluate environmental subsets over receding horizons, with variants using RRT*, random graphs, motion primitives, model predictive control, or tree rewiring.
  • Purely geometric reasoning may miss distinctions such as mud versus solid ground and visual cues including signage or task-relevant landmarks.
  • Semantic exploration methods either build semantic 3D maps before planning or retain geometric candidates and use semantics to rescore or search among them.
  • Map-based semantic planning enables global reasoning but incurs the cost and latency of dense semantic reconstruction.

III. Methodology

SGE uses semantic camera inputs to generate candidate waypoints and an exploration manager to select goals receding-horizon style. Candidates are validated, ordered by a TSP from the robot’s current position, and continuously updated during exploration.

  • SGE has a Waypoint Selection module and an Exploration Manager that continuously update decisions until environment coverage is achieved.
  • The Waypoint Selection module samples candidate goals from semantically segmented camera images identifying terrain types and obstacles.
  • Candidate waypoints are ordered by solving a TSP from the robot’s current position, and the first optimized waypoint becomes the next goal.
  • The waypoint list is continuously refreshed by adding new candidates and removing visited waypoints.

A. Semantically-guided Waypoint Selection

SGE selects exploration waypoints directly in the image plane using semantic and geometric utility cues, then projects sampled pixels into 3D for downstream planning. This avoids dense semantic volumetric reconstruction while preserving real-time operation.

  • Waypoint generation: SGE generates candidate exploration goals from semantically segmented RGB images and aligned depth measurements.Traversable regions, obstacles, and optional task-relevant objects are identified before sampling.
  • Utility formulation: Each traversable pixel receives a utility score combining terrain consistency, obstacle proximity, object-of-interest proximity, and depth-based exploration reward.The utility is rectified before normalization into a sampling distribution.
  • Utility terms: Neighboring traversable-pixel counts favor locally consistent terrain and reduce sampling near semantic boundaries or partial obstacles.The coefficient ca controls this spatial-consistency term and may be tuned by terrain type.
  • Utility terms: Obstacle-distance penalties repel samples from obstacle classes, with stronger effects near obstacles and smooth spatial decay.Distances use a 2D transform with O(V) computational complexity, where V is grid size.
  • Utility terms: Object proximity rewards waypoints near task-relevant objects, while depth rewards distant traversable regions to encourage outward exploration.Both terms provide smooth semantic or expansion bias without explicit volumetric entropy computation.
  • 3D projection: Sampled image pixels are projected into camera and global 3D frames using aligned depth and camera calibration before manager validation and optimization.Sampling complexity is O(n+mlog(n)), approximately O(n) when the number of samples m is small.

B. Exploration Manager

The Exploration Manager continuously validates sampled waypoints, orders valid goals with receding-horizon TSP optimization, and handles navigation uncertainty through temporary bans and graph-based relocation.

  • Manager architecture: The manager runs concurrent optimization and navigation loops that update goals, monitor execution, maintain taboo regions, and trigger relocation when needed.This keeps waypoint selection and navigation response continuously updated during exploration.
  • Optimization loop: The optimization loop samples, validates, stores, and repeatedly reorders waypoints using a TSP initialized at the robot’s current position.Validation checks include boundaries, traversability, taboo locations, and previously explored regions.
  • Optimization loop: The TSP uses Euclidean waypoint distances and incrementally updates its cost matrix, whose full evaluation has O(n^2) complexity.The default configuration omits return-to-start cost, and Guided Local Search supports real-time operation with up to 100 nodes.
  • Uncertainty handling: Navigation failures create temporary taboo regions after traversal-time thresholds are exceeded, and expired entries are removed so avoidance is temporary.The failure threshold is set at 150% of estimated traversal time.
  • Uncertainty handling: Relocation backtracks through a dynamically updated bidirectional graph of traversed positions when the optimized goal is far from the robot.A* identifies a route between the nearest visited nodes associated with the current and target positions.

IV. Experiments and Results

The evaluation combines simulation benchmarks with real-world deployments to assess volumetric exploration, image-based sampling behavior, and semantic task conditioning. Parameters remain fixed across experiments to test robustness across environments.

  • Evaluation design: SGE is evaluated against state-of-the-art exploration planners and in real-world deployments using volumetric, behavioral, and semantic task-conditioning criteria.The evaluation covers simulation benchmarks and physical robot deployments.
  • Evaluation design: The same empirically selected utility weights and waypoint sampling rate are used across simulation and real-world experiments.The configuration uses ca = 3, cb = 5, cc = 0.2, cd = 8, and eight sampled waypoints per time step.

A. Simulation Benchmark Evaluation

SGE achieved complete campus coverage in simulation and maintained exploration across semantic, ablation, and real-world deployment evaluations. Its semantic utility terms improved coverage consistency, while taboo regions and relocation supported fault-tolerant execution.

  • Simulation benchmark: SGE achieved complete campus coverage, with an average explored volume of 44423m3, comparable to TARE and substantially exceeding NBVP.Its longer path reflects shorter-range image-based goals and produces finer-grained coverage progression.
  • Ablation study: Removing any individual semantic utility component degraded mean explored volume, with depth reward supporting outward expansion and obstacle repulsion improving navigation reliability.Removing spatial consistency increased sensitivity to segmentation noise and destabilized waypoint selection.
  • Ablation study: 44423±659m3 was achieved by the full formulation, while ablated variants showed standard deviations up to an order of magnitude larger.Without the depth term, variability reached ±13743m3.
  • Ablation study: Disabling taboo regions or relocation caused incomplete exploration in 2 of 5 trials, demonstrating their fault-tolerance roles under execution uncertainty.Taboo regions prevented repeated attempts at inaccessible goals, while relocation enabled transitions between distant frontiers.
  • Semantic task conditioning: SGE allocated 39.46% of its forest trajectory to labeled trails, compared with 6.77% for TARE, while maintaining similar traversal distance.TARE explored slightly greater total volume in the 1200s trial, whereas SGE demonstrated controllable semantic biasing.
  • Real-world validation: Real-world deployments covered an indoor campus building and underground mines, including a 12835m3 limestone-mine run over 2116s and 765m.Exploration ended due to battery and hardware limits in both mine trials, while taboo and relocation mechanisms maintained continued operation despite segmentation uncertainty.

V. Conclusion and Future Work

SGE provides real-time exploration with volumetric coverage comparable to state-of-the-art geometric planners while adding task-conditioned behavior in structurally complex environments. The work positions semantic-guided exploration as an initial step toward fully learning-based exploration and identifies broader terrain evaluation and learned waypoint sampling as future directions.

  • SGE achieved reliable large-scale exploration in real time across simulation benchmarks and field deployments.
  • Volumetric coverage was comparable to state-of-the-art geometric planners, while SGE provided task-conditioned behavior unavailable to purely geometric methods.The distinction was particularly evident in structurally complex environments.
  • Future work will pursue terrain-aware cost adaptation, RGB-D-based direct waypoint sampling, and broader evaluation in extreme terrains.These directions support the transition toward fully learning-based exploration and stronger robustness testing.
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