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Dream-SLAM: Dreaming the Unseen for Active SLAM in Dynamic Environments

Xiangqi Meng, Pengxu Hou, Zhenjun Zhao, Javier Civera, Daniel Cremers, Hesheng Wang, Haoang Li

arXiv:2602.21967v1cs.ROcs.CV

TL;DR

Active SLAM depends on its underlying estimation modules, and planning from partially observed structures can leave information incomplete and cause detours. Dream-SLAM dreams cross-spatio-temporal images and plausible structures of unexplored areas for localization, mapping, and planning. It outperforms state-of-the-art approaches in localization and mapping accuracy and exploration efficiency.

  • Problem

    Active SLAM performance depends on underlying estimation modules, while planning from partially observed structures can leave information incomplete and cause detours.

    Source: §I · p. 1, B. Structure Dreaming-based Global… · p. 7

  • Method

    Dream-SLAM uses dreamed cross-spatio-temporal images for localization and Gaussian-based mapping, and integrates dreamed structures with observed geometry for long-horizon planning.

    Source: §VIII · p. 15

  • Results

    Dream-SLAM outperforms state-of-the-art approaches in localization and mapping accuracy and exploration efficiency; it completes exploration in 57.54 m, saving 14% distance compared with ActiveSplat*.

    Source: §I · p. 3, 2) Experimental Results · p. 15

  • Takeaways & Limitations

    Reasoning over semantically plausible structures of unexplored regions supports farsighted planning and efficient, thorough exploration.

    Source: §I · p. 2, 2) Experimental Results · p. 15

Abstract

from arXiv · show

In addition to the core tasks of simultaneous localization and mapping (SLAM), active SLAM additionally in- volves generating robot actions that enable effective and efficient exploration of unknown environments. However, existing active SLAM pipelines are limited by three main factors. First, they inherit the restrictions of the underlying SLAM modules that they may be using. Second, their motion planning strategies are typically shortsighted and lack long-term vision. Third, most approaches struggle to handle dynamic scenes. To address these limitations, we propose a novel monocular active SLAM method, Dream-SLAM, which is based on dreaming cross-spatio-temporal images and semantically plausible structures of partially observed dynamic environments. The generated cross-spatio-temporal im- ages are fused with real observations to mitigate noise and data incompleteness, leading to more accurate camera pose estimation and a more coherent 3D scene representation. Furthermore, we integrate dreamed and observed scene structures to enable long- horizon planning, producing farsighted trajectories that promote efficient and thorough exploration. Extensive experiments on both public and self-collected datasets demonstrate that Dream-SLAM outperforms state-of-the-art methods in localization accuracy, mapping quality, and exploration efficiency. Source code will be publicly available upon paper acceptance.

I. INTRODUCTION

Dream-SLAM addresses limitations in active SLAM by dreaming cross-spatio-temporal images and plausible unseen structures for localization, mapping, and planning. Experiments report improved localization and mapping accuracy and exploration efficiency over state-of-the-art approaches.

I. INTRODUCTION

  • Active SLAM is limited by dependence on underlying estimation modules, shortsighted planning over observed maps, and assumptions of static environments.The cited discussion links dynamic objects to occlusions and localization drift that degrade planning reliability and overall performance.Source: §I · p. 1
  • Prior efforts prioritize textured areas,, reason over unknown space,, or filter or reconstruct moving objects [16],,, each with stated trade-offs.The cited discussion notes exploration bias, simplified assumed layouts, instability under complex motion, or increased system complexity.Source: §I · p. 1
  • Dreamed cross-spatio-temporal images depict the previous 3D scene from the current viewpoint, enabling foreground constraints for current-camera pose estimation.The method uses a diffusion-based network to generate these images.Source: §I · p. 2
  • A feedforward network predicts per-pixel Gaussians for static background and dynamic foreground, which are refined using dreamed and real images for a coherent 3D representation.Source: §I · p. 2
  • For planning, Dream-SLAM inpaints incomplete views from virtual cameras at candidate waypoints, back-projects them into Gaussians, and integrates them with the map.Dreamed structures guide farsighted planning but are replaced by real observations when visited.Source: §I · p. 2
  • Dream-SLAM outperforms state-of-the-art approaches in localization and mapping accuracy and exploration efficiency across public and self-collected datasets.Source: §I · p. 3

II. RELATED WORK

The related work reviews passive SLAM representations and dynamic-scene methods, active exploration strategies, and the models underlying Dream-SLAM. It identifies limitations in foreground reconstruction and planning over unseen areas.

A. Passive SLAM

  • Classical and neural passive SLAM methods either filter dynamic objects or struggle to reconstruct dynamic foreground reliably; Dream-SLAM combines dreamed foreground constraints with per-pixel Gaussian prediction.The cited approaches include geometric, optical-flow, and semantic filtering, as well as methods modeling rigid or articulated motion.Source: A. Passive SLAM · p. 3

B. Active SLAM

  • Active SLAM methods use sampling-based or frontier-based planning, while approaches reasoning over unknown space may assume overly regular layouts.Dream-SLAM instead dreams semantically plausible structures of unexplored regions to support a more complete layout and farsighted paths.Source: B. Active SLAM · pp. 3–4

A. Preliminaries

  • Diffusion models learn to remove noise from latent codes in a reverse process after noise is gradually added in the forward process.Source: A. Preliminaries · p. 4
  • DUSt3R encodes two images and decodes their features to predict point clouds in the first camera frame.Source: A. Preliminaries · p. 4
  • Gaussian splatting represents a scene with parameterized 3D Gaussians and optimizes them by minimizing rendered-image appearance differences.The photometric loss combines L1 and SSIM terms, with alpha controlling their trade-off.Source: A. Preliminaries · p. 4

B. Dream-SLAM Overview

Dream-SLAM uses dreamed cross-spatio-temporal images to support localization and mapping in dynamic scenes, and dreamed structures of unexplored regions to guide long-horizon exploration. Its pipeline combines RGB observations, Gaussian scene representations, and diffusion-based image inpainting.

B. Dream-SLAM Overview

  • Dream-SLAM takes RGB images without depth, segments dynamic objects, and supports rigid and non-rigid objects; it also uses bundle adjustment and loop closure.Humans are the main illustrative example, with other object categories reported in supplementary material.Source: B. Dream-SLAM Overview · p. 4
  • A feedforward network estimates pixel-wise Gaussians, which are optimized using multi-view photometric constraints from real and dreamed images.The overview describes this as a way to map foreground and background efficiently and photorealistically.Source: B. Dream-SLAM Overview · p. 4
  • For exploration, Dream-SLAM inpaints images rendered at unvisited waypoints, reconstructs Gaussians for unseen areas, and integrates them into the map for farsighted planning.The enriched Gaussian map is used to update a topological map and plan a path.Source: B. Dream-SLAM Overview · p. 4

A. Dreaming Cross-spatio-temporal Images

  • Cross-spatio-temporal images show the previous scene from the current camera viewpoint, letting pose estimation use both dynamic foreground and static background.The method presents these images as a way to address inconsistent foreground states between observations and the existing map.Source: Figure 2 · p. 4, A. Dreaming Cross-spatio-temporal Images · p. 5
  • The inpainting mask is dilated around the current foreground to cover regions to remove and foreground areas to fuse from the earlier time.The diffusion model uses both time-adjacent images and this mask to dream the cross-spatio-temporal image.Source: A. Dreaming Cross-spatio-temporal Images · p. 5, Figure 3 · p. 5

B. Camera Localization

Camera localization estimates the relative pose between successive frames using geometric and photometric constraints. The photometric constraint compares a rendered prior scene, including its dynamic foreground, with a dreamed cross-spatio-temporal image.

Camera Localization

  • The next camera pose is computed by composing the estimated relative transformation with the previous camera pose.The relative transformation is estimated from photometric and geometric constraints.Source: B. Camera Localization · p. 5

Photometric Constraints

  • The photometric constraint uses the dreamed cross-spatio-temporal image to supervise both dynamic foreground and static background when optimizing the relative camera transformation.The rendered Gaussians are transformed into the current camera frame before comparison with the dreamed image.Source: B. Camera Localization · pp. 5–6

Geometric Constraints

  • Point correspondences from paired Gaussian point clouds provide a geometric constraint, and SVD supplies the initial relative transformation for photometric optimization.The method says this strategy uses dynamic foreground to compensate for noise and increases point-cloud overlap and alignment constraints.Source: B. Camera Localization · p. 6

C. 3D Scene Mapping

Dream-SLAM predicts foreground and background Gaussians with a feedforward network and refines them using real and dreamed multi-view image constraints. The mapping approach is designed to be efficient while maintaining accuracy.

Feedforward Gaussian Prediction

  • A feedforward network predicts per-pixel Gaussians for dynamic foreground and static background, improving efficiency while maintaining high accuracy compared with classical Gaussian splatting-based SLAM,.The network also uses dreamed cross-spatio-temporal images to refine the predicted Gaussians.Source: C. 3D Scene Mapping · p. 6
  • The network predicts Gaussian centers from image tokens, then combines point and image features to estimate Gaussian attributes and confidences.The described architecture uses position heads, point tokens, and Gaussian heads.Source: C. 3D Scene Mapping · p. 6
  • Predicted Gaussians are trained with photometric constraints from rendered images and complementary geometric loss on extracted point clouds.The photometric training loss compares a rendered image with its ground-truth image.Source: C. 3D Scene Mapping · pp. 6–7

Gaussian Refinement

  • Refinement combines real image pairs with dreamed cross-spatio-temporal views as multi-view photometric constraints on the time t+1 Gaussians.The authors report that two dreamed images achieve their best observed balance between accuracy and efficiency.Source: C. 3D Scene Mapping · p. 7

V. EXPLORATION PLANNING

Exploration planning dreams semantically plausible structures in unexplored regions and uses them to make decisions. The authors state that this strategy can reduce path length and improve exploration completeness.

V. EXPLORATION PLANNING

  • The planning approach dreams semantically plausible structures of unexplored regions and uses them to make decisions.The dreamed structures are the section’s stated technical contribution to farsighted exploration.Source: §V · p. 7
  • The authors state that the strategy can significantly reduce the path length.This is presented as an intended benefit of using dreamed structures for planning.Source: §V · p. 7
  • The authors state that the strategy can improve exploration completeness.The section links this benefit to dreaming structures in unexplored regions and leveraging them for decisions.Source: §V · p. 7

A. Exploration Planning Pipeline

Dream-SLAM plans exploration by combining observed maps with dreamed structures of unexplored areas. It uses selected, semantically inpainted views to predict Gaussians that enrich the map for farsighted planning.

A. Exploration Planning Pipeline

  • The planner builds a topological map from traversable space, then globally orders waypoint sub-regions and locally prioritizes under-explored areas.It replans as newly observed and dreamed sub-regions are added.Source: A. Exploration Planning Pipeline · p. 7

C. Semantically Plausible Image Inpainting

B. Structure Dreaming-based Global Ordering

  • Dreamed structures enrich the map and refine global ordering, reducing unnecessary detours and yielding shorter paths for long-horizon planning.After visiting a dreamed sub-region, real observations replace its dreamed representation.Source: B. Structure Dreaming-based Global… · pp. 7–8, Figure 6 · p. 8
  • Planning handles dynamic scenes by using static-background structures for path planning and dreaming, while routing around mapped dynamic obstacles.Dijkstra’s algorithm receives dynamic-obstacle shapes and relative locations to support obstacle avoidance.Source: B. Structure Dreaming-based Global… · p. 8

VI. EXPERIMENTS ON PUBLIC DATASETS

Public-dataset experiments evaluate localization and mapping separately from exploration planning because no public dataset jointly evaluates both. The experiments cover dynamic RGB-D benchmarks and assess trajectory error and rendering quality.

VI. EXPERIMENTS ON PUBLIC DATASETS

  • Public-dataset experiments assess localization and mapping separately from exploration planning because no public dataset evaluates both together.The experiments use a server with an E3-1226 CPU and RTX 4090 GPU.Source: §VI · p. 9

1) Experimental Setup:

  • TUM and Bonn benchmark SLAM in dynamic scenes; Bonn additionally includes human–object interactions and sequences with larger human movements.TUM contains indoor sequences with people walking and provides ground-truth camera trajectories.Source: 1) Experimental Setup · p. 9
  • Localization is evaluated with absolute trajectory error RMSE and standard deviation in centimeters, while mapping is assessed through novel-view rendering quality.PSNR, SSIM, and LPIPS measure differences between rendered and ground-truth images in qualitative analysis.Source: 1) Experimental Setup · p. 9
  • The comparison includes RGB-D and RGB methods, with Dream-SLAM among the RGB-only methods; its dynamic-scene mapping is also shown in novel-view renderings.ORB-SLAM3, Rodyn-SLAM, and PG-SLAM use RGB-D input, whereas MonST3R, WildGS-SLAM, and Dream-SLAM use RGB images.Source: 1) Experimental Setup · p. 10, Figure 7 · p. 10

2) Localization Results:

Dream-SLAM reports the highest localization accuracy on Bonn and surpasses competing methods on TUM. It also reports better foreground and background reconstruction, attributing mapping quality to deep geometry and more accurate camera poses.

2) Localization Results:

  • Dream-SLAM achieves the highest localization accuracy on Bonn and surpasses WildGS-SLAM on TUM by using cross-spatio-temporal observations and foreground and background information.On Bonn, virtual observation constraints support more effective use of foreground information.Source: 2) Localization Results · p. 10

3) Mapping Results:

  • Rendering quality is evaluated over entire images and with foreground regions masked out, alongside qualitative novel-view comparisons.Different methods’ reconstructions are rendered from the same viewpoint for comparison.Source: 3) Mapping Results · p. 10, Table II · p. 11
  • Dream-SLAM achieves better foreground and background reconstruction, using deep geometry instead of depth measurements and more accurate camera poses.The deep geometry model avoids depth outliers and better preserves spatial relationships between objects; the tracking strategy also improves reconstruction quality.Source: 3) Mapping Results · pp. 10–11

4) Efficiency Evaluation:

On a Bonn sequence with non-rigid human, rigid box, and static background, Dream-SLAM reports the highest efficiency, with a 0.65 s total runtime. Exploration experiments use Gibson and HM3D, including scenes augmented with dynamic humans.

4) Efficiency Evaluation:

  • 0.65 s total runtime makes Dream-SLAM the most efficient method on Bonn’s mv_box sequence; dreaming cross-spatio-temporal images accounts for about 0.3 s.The authors attribute other methods’ slower runtimes to uncertainty-map prediction or online mapping-network training, and report fewer localization optimization iterations for Dream-SLAM.Source: 4) Efficiency Evaluation · p. 11

1) Experimental Setup:

  • Exploration is evaluated on Gibson and HM3D scenes, with dynamic humans added because the datasets originally lack them.HM3D features larger and more complicated scenes than Gibson.Source: 1) Experimental Setup · p. 11
  • Exploration metrics are completeness ratio and total path length; dynamic-scene path length is not reported for methods that fail to explore the entire environment.Top-view figures encode completeness ratio, traversed path length, path proportion, and exploration time by trajectory color.Source: 1) Experimental Setup · p. 11, Figure 8 · p. 12, Figure 9 · p. 12

2) Evaluation Results:

Dream-SLAM achieves the strongest exploration completeness and path-cost results across the evaluated sequences. Ablations also support the localization benefit of dreamed cross-spatio-temporal images.

2) Evaluation Results:

  • Across both datasets, Dream-SLAM achieves the highest exploration completeness and lowest path cost across all sequences.The authors attribute this to farsighted planning over semantically plausible structures of unexplored areas.Source: 2) Evaluation Results · p. 13

C. Ablation Study

  • The ablation study is designed to validate the effectiveness of the proposed modules and strategies.The supplied passage does not report specific ablation outcomes for this subsection.Source: C. Ablation Study · p. 13

1) Localization Using Dreamed Images:

  • Using dreamed cross-spatio-temporal images achieves higher camera-localization accuracy than the version without them.The authors say the result supports the reliability of image dreaming and the value of foreground information for localization.Source: 1) Localization Using Dreamed Images · p. 14

2) Mapping Using Dreamed Images:

On self-collected data, Dream-SLAM explores the home environment with a shorter path than ActiveSplat* and significantly outperforms WildGS-SLAM in camera localization. Its dreamed images and structures also support foreground reconstruction and long-horizon planning.

2) Mapping Using Dreamed Images:

3) Planning Using Dreamed Structures:

  • Reasoning over dreamed semantically plausible structures enables thorough exploration with reduced path length and superior exploration efficiency.This is the reported planning ablation against the version without dreamed structures.Source: 3) Planning Using Dreamed Structures · p. 14

2) Experimental Results:

1) Experimental Setup:

  • The self-collected test environment is a home with a living room, kitchen, three bedrooms, two bathrooms, and moving humans.The robot uses a front-facing D455 camera and Livox Mid-360 LiDAR; computation runs on a networked server with an RTX 4090 GPU.Source: 1) Experimental Setup · p. 14
  • ActiveSplat* combines ActiveSplat planning with WildGS-SLAM localization for dynamic scenes; the comparison uses the same starting point.The independent localization comparison uses the same prerecorded image sequence for both methods.Source: 1) Experimental Setup · p. 14

2) Experimental Results:

  • 57.54 m, 14% less distance than ActiveSplat* for full exploration of the self-collected environment.Dream-SLAM’s route avoids ActiveSplat*’s locally optimal path, which overlooks the kitchen and several bedrooms.Source: 2) Experimental Results · p. 15

VIII. CONCLUSIONS

  • Dream-SLAM combines dreamed images for localization and Gaussian refinement with dreamed structures for long-horizon exploration planning.The conclusion reports that experiments on public and self-collected datasets outperform state-of-the-art approaches.Source: §VIII · p. 15
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