ON as ALC: Active Loop Closing Object Goal Navigation

Daiki Iwata; Kanji Tanaka; Kouki Terashima; Shoya Miyazaki

REVIEW 3 major objections 4 minor 21 references

ON as ALC: Active Loop Closing Object Goal Navigation

T0 review · 3 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that long-distance active loop closing — steering a robot back to visited points to reset drift — is best solved by reframing it as object-goal navigation, with a blended planner beating either objective alone.

desk verdict Nice framing, but the experimental evidence doesn't support the LD-ALC claim yet; the local reduction and single-table results are load-bearing. read the letter →

arxiv 2412.11523 v2 pith:HBV6WCDY submitted 2024-12-16 cs.RO

Daiki Iwata , Kanji Tanaka , Shoya Miyazaki , Kouki Terashima This is my paper · ORCID

classification cs.RO

keywords activeloopclosingobjectgoalnavigationmap-lesssubgoalregressionlearning-basedwaypointplanningsuccessweightedbypathlengthHabitat-Simpriormapuncertainty

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Long-distance active loop closing — steering a robot back to previously visited points so it can reset the drift errors accumulated in its incrementally built map — fails precisely when the map is most needed, because the map itself is corrupted by that accumulated drift. The paper claims this problem is better attacked with mapless object-goal navigation (ON), the embodied-AI task of finding a target object from a query image without a prior map, and it proposes ALCON, which starts from an off-the-shelf frontier-guided ON planner, extends it to consume a prior map, and fuses the two subgoal predictions with a weight set automatically by the map's uncertainty estimate K. In the authors' augmented Habitat-Sim test bed, the fused planner reaches a success-weighted path length (SPL) of 0.413, ahead of the ALC-only (0.404), ON-only (0.383), and random-frontier (0.359) alternatives. The paper itself states the load-bearing caveat: the experiments run in local episodes confined to the error ellipse around the target, and validation on full long-distance active SLAM tasks is deferred to a follow-up study. If the central claim holds, the growing family of ON planners — data-driven, frontier-guided, and LLM-guided — becomes a practical engine for a long-standing SLAM control problem.

What carries the argument

The load-bearing object is the score map: a single-channel 480×480 grid at 0.1 m resolution with 256 levels that compresses what the robot's visual experience contributes to planning. A training-free planner (TFP) produces it by scoring candidate viewpoints with the semantic similarity, in BERT embedding space, between the target image and the images seen from those viewpoints, recording scores at cluster centroids, with MiDaS providing pseudo-depth so the RGB-only camera can build the map. A reinforcement-learning planner (RLP), an actor-critic CNN that regresses 2D subgoal coordinates in the style of the Active Neural SLAM framework, reads this score map and acts as a monitor that pulls the TFP's myopic, high-resolution choice toward a long-horizon one when the two disagree by more than $G = 5$ m. The two planning streams are joined by the convex blend $p = p_{\mathrm{ALC}} w_{\mathrm{ALC}} + p_{\mathrm{ON}} (1-w_{\mathrm{ALC}})$, where the single scalar $w_{\mathrm{ALC}}$ is the entire arbitration mechanism between returning to the loop-closing point and continuing to explore for the object; the paper says it is computed from the prior map's uncertainty estimate $K$, though the explicit formula is cut off in the text, and the companion weight $w_M = 0.7$ in the TFP-RLP fusion is acknowledged to be unoptimized. Around this core sits an augmented-reality training scheme that synthesizes score maps and obstacle maps by data augmentation and builds each episode only inside the error ellipse around the target, on the argument that data outside that ellipse cannot affect the planner's decisions.

What would settle it

Re-run the same planners on a full long-distance benchmark in which the prior map is corrupted increasingly as travel distance grows — for instance, positional error proportional to path length plus unobserved terrain between start and target — and compare SPL with the local-episode numbers; if the fused planner's advantage over the ALC-only ablation shrinks or reverses as distance grows, the local reduction has not captured the long-distance ALC (LD-ALC) difficulty the paper claims to address.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that active loop closing and object-goal navigation are the same decision problem viewed from two sides, so the ON toolkit can be handed the ALC task wholesale — a direction the paper positions as the first application of mapless navigation to ALC. A previously visited point (PVP) whose map coordinate is corrupted by drift plays the role of the target object in an instance-level image-goal navigation task, and the semantic relationship between the PVP and nearby landmark objects becomes the exploration cue: the robot can find a coffee maker by first surveying the kitchen, an ALC behavior the paper illustrates explicitly. Concretely, a training-free semantic-frontier planner produces a single-channel score map from BERT-embedding similarity between viewpoint images and the target image, with MiDaS supplying pseudo-depth for the RGB camera, and two reinforcement-learning planners trained in the Active Neural SLAM style regress subgoal coordinates from that score map, one supervised by an ON loss against true target positions and one by an ALC loss against positions drawn inside the uncertainty radius K of the corrupted PVP. At run time the two regressors' outputs are blended by $p = p_{\mathrm{ALC}} w_{\mathrm{ALC}} + p_{\mathrm{ON}} (1-w_{\mathrm{ALC}})$, with the weight $w_{\mathrm{ALC}}$ derived from $K$, and the nearest frontier cell to the blend becomes the next subgoal. The reported result is that the blend, assisted by the ON subtask, outperforms both single-objective ablations and the random-frontier baseline in terms of SPL (success weighted by path length) in the authors' augmented simulator.

Load-bearing premise

The entire result rests on the assumption that a short, local episode confined to the error ellipse around the target reproduces the real difficulty of long-distance loop closing — drift accumulating along the route and unfamiliar terrain crossed en route — an assumption the paper itself does not yet validate.

Editorial extensions

If this is right

In the tested workspace, the uncertainty-weighted fusion of the ON and ALC planners yields higher SPL than the ON-only planner, the ALC-only planner, or the random-frontier planner, so ALC performance can be improved without designing a new map-based exploration heuristic.
Because the ON component relies on semantic cues rather than map coordinates, its contribution should degrade more slowly than map-based ALC as the prior map's drift uncertainty $K$ grows, which is exactly the long-distance regime the paper targets.
The uncertainty-driven weight $w_{\mathrm{ALC}}$ replaces hand-tuned arbitration between exploration and loop closure with a single scalar derived from the map's own error estimate.
The augmented-reality training scheme makes long-distance ALC training tractable: episodes generated only inside the error ellipse, plus data augmentation over synthetic score and obstacle maps, avoid simulating full long-distance workspaces.
By reframing ALC as an instance-level image-goal navigation problem, the paper opens loop closing to the ON community's whole toolkit — data-driven, frontier-guided, and LLM-guided planners can in principle be adapted to ALC the same way.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The single-weight fusion rule is a general template: any two subgoal regressors with complementary failure modes could be arbitrated by a scalar uncertainty estimate, so the same scheme should transfer to multi-objective ON tasks that must balance landmark revisits against exploration of unknown areas.
The local-episode reduction makes a direct scaling prediction the paper does not test: if the error-ellipse restriction is faithful, ALCON's SPL advantage should survive when the same episode is embedded in a full long-distance trajectory with drift growing along the route; a dedicated full-journey benchmark would settle this.
A natural testable extension is to replace the fixed-radius disk reward with a learned model of how much each candidate revisit would reduce map drift, making the ALC reward itself a prediction instead of a heuristic.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The core idea is genuinely worth a look: recasting active loop closing as an object-goal navigation problem, so that semantic, mapless cues can guide a robot toward previously visited points even when the prior map is drifting. That is a new framing, and the uncertainty-weighted combination of ALC and ON losses/rewards is a sensible way to blend the two objectives. I also give the authors credit for being explicit that full active-SLAM validation is future work, not something this paper delivers.

The problem is that the paper's headline claim—"significantly outperformed" all baselines in long-distance ALC—rests on evidence that doesn't reach that conclusion. Section III-E reduces LD-ALC to a local episode inside a single Habitat-Sim workspace, with the argument that data outside the error ellipse cannot influence decision-making. That argument doesn't survive contact with the actual long-distance problem: drift accumulates along the route before you reach the PVP neighborhood, and that accumulation changes the prior map, the choice of which PVP to revisit, and the planner's localization confidence. Omitting the travel is not harmless; it makes the SPL numbers measure local subgoal selection, not LD-ALC.

Even for the local claim, Table I gives one SPL value per method, with no episode count, no seeds, and no significance test. The margin over the ALC ablation is 0.009, which could easily be noise. There are also smaller problems: the weighting coefficient wM=0.7 is said to be naive and unoptimized, and the description of wALC in Eq. (4) has a typo ("fALC and fALC") and an incomplete sentence about where the uncertainty estimate comes from.

What is good here is the direction, not the evidence. The authors show a plausible path toward using ON technology for ALC, and they are transparent about what they did and did not validate. But as it stands, the central quantitative claim is unsupported. A serious referee should ask for multi-seed experiments with error bars and significance tests, additional ALC baselines (a frontier-based planner, for instance), and—ideally—an evaluation that actually involves long-distance travel or, at minimum, an explicit justification for why the local reduction preserves the difficulty of LD-ALC.

Who is this for? Researchers working on active SLAM or embodied navigation who want to see a fresh connection between two subfields will get value from the idea and the experimental setup. It is a useful position piece, but not yet a solid systems paper. I would send it to peer review, but with the expectation of major revision or a significant re-scoping. I would not cite it in my own work yet.

Referee Report

3 major / 4 minor

Summary. The paper proposes ALCON, a method that recasts active loop closing (ALC) as an object-goal navigation (ON) problem. A training-free frontier planner (TFP) supplies score maps, and two reinforcement-learning planners are trained with an ALC loss/reward and an ON loss/reward, with a weighted combination governed by a prior-map uncertainty estimate. The declared goal is long-distance ALC (LD-ALC), where map drift is large. Experiments in a single Habitat-Sim workspace compare ALCON against an ON-only ablation, an ALC-only ablation, and a random-frontier baseline, reporting SPL values; the paper claims ALCON significantly outperforms all baselines. The paper also states that validation of the full active SLAM task for long-distance travel is deferred to a follow-up study.

Significance. The conceptual direction is interesting and timely: integrating mapless, semantically driven ON techniques into map-based active SLAM could open a useful line of work. The proposed method is simple, and the loss/reward decomposition is clearly described. However, the evidence presented does not support the paper's central LD-ALC claim. The reported experiments test only local subgoal selection in a single scene, with no error bars, no episode counts, no seeds, and no statistical significance testing. If validated with proper long-distance active-SLAM experiments, the contribution could be of value to the embodied-AI and active-SLAM communities, but as it stands the paper's headline claim outruns its evidence.

major comments (3)

[Section IV, Table I] The central claim that ALCON 'significantly outperformed' all baselines is not supported by the presented data. Table I reports one SPL value per planner with no number of episodes, no random seeds, no standard deviations or confidence intervals, and no significance test. The margin over the ALC ablation is only 0.009, which is easily within run-to-run noise for a stochastic simulator. Please report distributions over episodes and seeds, perform a significance test, and ideally evaluate on multiple scenes; alternatively, weaken the claim to a qualitative observation.
[Section III-E and Section IV] The LD-ALC problem defined in Section I is not what the experiments measure. Section III-E reduces long-distance travel to a local episode in a single Habitat-Sim workspace around the target PVP, arguing that data outside the error ellipse 'has no influence on the decision-making.' This argument ignores the fact that, in a true long-distance episode, the accumulated drift that defines the ellipse also degrades the prior map along the entire route, affects the choice of which PVP to revisit, and determines the cost of reaching the PVP neighborhood through unfamiliar regions. The reported SPL numbers therefore measure local subgoal-selection efficiency, not LD-ALC performance. The paper itself concedes in Section IV that 'performance validation of the full active SLAM tasks' is left to a follow-up study. Either provide full LD-ALC experiments, or explicitly reframe the title and claims as addressing local ALC subgoal selection.
[Section III-D and Section IV] The proposed method's behavior depends on several manually set coefficients and thresholds (wM = 0.7, k = 0.1, r' = 2 m, disk score values 255/150/50, and wALC), and Section III-C states that wM is 'based on a naive approach and has not yet been optimized.' Given that the performance advantage over the ALC ablation is 0.009 SPL, it is unclear whether the result is robust to reasonable changes in these parameters. Please add a sensitivity analysis or a systematic ablation over the main free coefficients; otherwise the claimed advantage may be an artifact of a particular manual setting.

minor comments (4)

[Section III-D] Near Eq. (4), the text states that the next-best-subgoal is determined by the weighted sum of the regression results from 'fALC and fALC'; this should almost certainly read 'fALC and fON'.
[Section IV] The caption of Table I has the typo 'PERFORMACNE'; it should read 'PERFORMANCE'.
[Section IV] The experimental section does not state how many test episodes were run, how the uncertainty magnitude K was sampled, or whether the reported SPL is an average over a fixed set of episodes. These details are needed for reproducibility.
[Section III-E] The phrase 'data outside these error ellipse areas has no influence on the decision-making' needs a more careful justification; even in a local problem, obstacles and score structure outside a fixed ellipse can affect the shortest path to a target inside it.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the training and evaluation pipeline is self-contained supervised learning, and the cited self-work is not load-bearing. The paper's LD-ALC generalization gap is an external-validity limitation, not a derivation that reduces to its inputs.

full rationale

The paper contains no analytic derivation whose output is equivalent to an input by construction. The RLP in Section III-C is trained by supervised regression against ground-truth subgoal coordinates read off the score maps (Section III-D and the training description in Section IV), and the reported SPL in Table I is an independent empirical benchmark among Ours, ALC, ON, and RF; minimizing a training loss does not by itself force the SPL ordering, so no fitted value is renamed as a prediction. The two self-citations ([13], [21]) are not load-bearing: [13] is a general pointer to combining map-based and mapless navigation ideas, and [21] supplies the 100-image RPG set used in testing, but neither is invoked as a theorem, a uniqueness result, or a substitute for the measurements. The paper's main weakness is external validity rather than circularity: Section III-E reduces LD-ALC to a local 'augmented ON workspace' by asserting that data outside the error ellipse cannot influence decision-making, and Section IV concedes that 'the performance validation of the full active SLAM tasks for long-distance travel and long-term autonomy is planned to be reported in a follow-up study.' These passages flag a generalization/assumption gap, not a case in which the claimed prediction is defined as the fitted input. The absence of error bars, episode counts, and significance tests in Table I is an evidence-quality concern that may undermine the strength of the comparison, but it is not a circular-reasoning concern. Accordingly, no circular step can be exhibited and the circularity score is 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim depends on several unvalidated modeling choices: the availability of K, the local-workspace approximation of long-distance ALC, the fidelity of the reimplemented TFP, and the disk-based score map used for training. These are not fatal on their own, but they mean the reported SPL gains are demonstrated only in a heavily simplified setting.

free parameters (5)

wM = 0.7
Weight between TFP and RLP predictions in Eq. (1). Declared 'naive and not yet optimized' in Section III-C(2). Affects the final subgoal and therefore path efficiency.
k = 0.1
Scale of the state-value loss term in Eq. (3). Set by hand, no ablation.
r' = 2 m
Radius of the score disks for ON rewards and ALC score maps; hand-set in Sections III-C(5) and III-D.
score values = 50, 150, 255
Hand-assigned intensities for the top three semantic clusters in the ON score map; Section III-D.
wALC = unspecified
Uncertainty-adaptive weight in Eq. (4); the mapping from K to wALC is not given in the text, so this is an unstated free choice that directly controls the blend of ALC and ON subgoals.

assumptions (4)

domain assumption Uncertainty K of the target PVP position is available as an attribute of the prior map, e.g. from the error covariance of a Kalman-filter SLAM.
Invoked in Section IV to generate ALC score maps and to set the blend weight. This may hold in classical SLAM but is not demonstrated in the simulator.
domain assumption The error ellipse around the PVP can be approximated by a single Habitat-Sim workspace, so episodes can be restricted to a small local area.
Stated in Section III-E. This reduction is what makes the experiments tractable, but it sidesteps the long-distance travel and accumulated error dynamics central to LD-ALC.
domain assumption The reimplemented TFP (MiDaS pseudo-depth + BERT embedding) preserves the semantic frontier behavior of the original TFP.
Section III-B lists two modifications but provides no validation that the resulting score maps are comparable to [11].
ad hoc to paper A score map with three hand-placed disks is a sufficient training signal for subgoal regression.
Section III-E generates 3000 training score maps by randomly placing three disks. This design is tailored to this paper and is not derived from the ALC or ON literature.

how reviews work

0 comments

Cite this review

Pith. "Pith review of ON as ALC: Active Loop Closing Object Goal Navigation." pith.science (2026). https://pith.science/paper/HBV6WCDY

@misc{pith2026241211523,
  author       = {Pith},
  title        = {Pith review of: ON as ALC: Active Loop Closing Object Goal Navigation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HBV6WCDY}},
  note         = {Machine review of arXiv:2412.11523}
}

read the original abstract

In simultaneous localization and mapping, active loop closing (ALC) is an active vision problem that aims to visually guide a robot to maximize the chances of revisiting previously visited points, thereby resetting the drift errors accumulated in the incrementally built map during travel. However, current mainstream navigation strategies that leverage such incomplete maps as workspace prior knowledge often fail in modern long-term autonomy long-distance travel scenarios where map accumulation errors become significant. To address these limitations of map-based navigation, this paper is the first to explore mapless navigation in the embodied AI field, in particular, to utilize object-goal navigation (commonly abbreviated as ON, ObjNav, or OGN) techniques that efficiently explore target objects without using such a prior map. Specifically, in this work, we start from an off-the-shelf mapless ON planner, extend it to utilize a prior map, and further show that the performance in long-distance ALC (LD-ALC) can be maximized by minimizing ``ALC loss" and ``ON loss". This study highlights a simple and effective approach, called ALC-ON (ALCON), to accelerate the progress of challenging long-distance ALC technology by leveraging the growing frontier-guided, data-driven, and LLM-guided ON technologies.

Figures

Figures reproduced from arXiv: 2412.11523 by the authors.

**Figure 2.** System overview. The ALC planner is formulated as an a [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** ALC taxonomy. The robot’s viewpoint path is depicted [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Augmented ON workspace. We generate the workspace fo [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: Experimental Setup. Top: Bird’s-eye view of the work [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

21 extracted references · 20 canonical work pages

[1]

Tard´ os, and Jos´ e A

Jos´ e Neira, Juan D. Tard´ os, and Jos´ e A. Castellanos. L inear time vehicle relocation in SLAM. In Proceedings of the 2003 IEEE International Conference on Robotics and Automation, ICRA 2003, September 14-19, 2003, Taipei, Taiwan , pages 427–433. IEEE, 2003

work page 2003
[2]

Vision meets robotics: The kitti dataset

Andreas Geiger, Philip Lenz, Christoph Stiller, and Raq uel Urtasun. Vision meets robotics: The kitti dataset. The International Journal of Robotics Research, 32(11):1231–1237, 2013

work page 2013
[3]

Highly scalable appearanc e- only slam-fab-map 2.0

Mark Cummins and Paul Newman. Highly scalable appearanc e- only slam-fab-map 2.0. In Robotics: Science and systems , volume 5, page 17. Seattle, USA, 2009

work page 2009
[4]

Activ e markov localization for mobile robots

Dieter Fox, Wolfram Burgard, and Sebastian Thrun. Activ e markov localization for mobile robots. Robotics and Autonomous Systems , 25(3-4):195–207, 1998

work page 1998
[5]

Exp loration with active loop-closing for fastslam

Cyrill Stachniss, Dirk Hahnel, and Wolfram Burgard. Exp loration with active loop-closing for fastslam. In 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(IEEE C at. No. 04CH37566), volume 2, pages 1505–1510. IEEE, 2004

work page 2004
[6]

Active slam a nd loop prediction with the segmented map using simpliﬁed models

Nathaniel Fairﬁeld and David Wettergreen. Active slam a nd loop prediction with the segmented map using simpliﬁed models. I n Field and Service Robotics: Results of the 7th International Conf erence, pages 173–182. Springer, 2010

work page 2010
[7]

Active slam using connectivity graph s as priors

Alberto Soragna, Marco Baldini, Dominik Joho, Rainer K¨ ummerle, and Giorgio Grisetti. Active slam using connectivity graph s as priors. In 2019 IEEE/RSJ International Conference on Intelligent Rob ots and Systems (IROS) , pages 340–346. IEEE, 2019

work page 2019
[8]

A sur vey on active simultaneous localization and mapping: State of the art and new frontiers

Julio A Placed, Jared Strader, Henry Carrillo, Nikolay A tanasov, V adim Indelman, Luca Carlone, and Jos´ e A Castellanos. A sur vey on active simultaneous localization and mapping: State of the art and new frontiers. IEEE Transactions on Robotics , 39(3):1686–1705, 2023

work page 2023

Show all 21 references

[9]

A survey of obj ect goal navigation

Jingwen Sun, Jing Wu, Ze Ji, and Y u-Kun Lai. A survey of obj ect goal navigation. IEEE Transactions on Automation Science and Engineering, 2024

2024
[10]

Learning to explore using active neural slam

Devendra Singh Chaplot, Dhiraj Gandhi, Saurabh Gupta, Abhinav Gupta, and Ruslan Salakhutdinov. Learning to explore using active neural slam. In International Conference on Learning Representations (ICLR), 2020

2020
[11]

How To Not Train Y our Dragon: Training-free Embod ied Object Goal Navigation with Semantic Frontiers

Junting Chen, Guohao Li, Suryansh Kumar, Bernard Ghane m, and Fisher Y u. How To Not Train Y our Dragon: Training-free Embod ied Object Goal Navigation with Semantic Frontiers. In Proceedings of Robotics: Science and Systems , Daegu, Republic of Korea, July 2023

2023
[12]

cat-shaped mug

Vishnu Sashank Dorbala, James F Mullen Jr, and Dinesh Ma nocha. Can an embodied agent ﬁnd your “cat-shaped mug”? llm-based z ero- shot object navigation. IEEE Robotics and Automation Letters , 2023

2023
[13]

Active robot vision for distant object change d etection: A lightweight training simulator inspired by multi-armed b andits

Kouki Terashima, Kanji Tanaka, Ryogo Y amamoto, and Jon athan Tay Y u Liang. Active robot vision for distant object change d etection: A lightweight training simulator inspired by multi-armed b andits. CoRR, abs/2307.14105, 2023

2023 arXiv
[14]

Active loop closure for osm-guided robotic mapping in large -scale urban environment

Wei Gao, Zezhou Sun, Mingle Zhao, Chengzhong Xu, and Hui Kong. Active loop closure for osm-guided robotic mapping in large -scale urban environment. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2024

2024
[15]

A frontier-based approach for autonom ous ex- ploration

Brian Y amauchi. A frontier-based approach for autonom ous ex- ploration. In Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIR A’97 - Towards New Computational Principles for Robotics and Auto mation, July 10-11, 1997, M...

1997
[16]

End-to-end (instance)-image goal navi- gation through correspondence as an emergent phenomenon

Guillaume Bono, Leonid Antsfeld, Boris Chidlovskii, P hilippe Wein- zaepfel, and Christian Wolf. End-to-end (instance)-image goal navi- gation through correspondence as an emergent phenomenon. I n The Twelfth International Conference on Learning Representat ions, ICLR 2024, Vi...

2024
[17]

Multi-o bject naviga- tion using potential target position policy function

Haitao Zeng, Xinhang Song, and Shuqiang Jiang. Multi-o bject naviga- tion using potential target position policy function. IEEE Transactions on Image Processing , 32:2608–2619, 2023

2023
[18]

Bert: Pre-training of deep bidirectional transformers for language understanding

Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Tou tanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of naacL-HLT , volume 1. Minneapolis, Minnesota, 2019

2019
[19]

Midas v 3

Reiner Birkl, Diana Wofk, and Matthias M¨ uller. Midas v 3. 1–a model zoo for robust monocular relative depth estimation. arXiv preprint arXiv:2307.14460, 2023

2023 arXiv
[20]

Habitat: A platform for embodied ai re search

Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, et al. Habitat: A platform for embodied ai re search. In Proceedings of the IEEE/CVF international conference on co mputer vision...

2019
[21]

Con: Continual object navigation via data- free inter-agent knowledge transfer in unseen and unfamili ar places, 2024

Kouki Terashima, Daiki Iwata, Kanji Tanaka, Shoya Miya zaki, and Jonathan Tay Y u Liang. Con: Continual object navigation via data- free inter-agent knowledge transfer in unseen and unfamili ar places, 2024

2024

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

Tard´ os, and Jos´ e A

Jos´ e Neira, Juan D. Tard´ os, and Jos´ e A. Castellanos. L inear time vehicle relocation in SLAM. In Proceedings of the 2003 IEEE International Conference on Robotics and Automation, ICRA 2003, September 14-19, 2003, Taipei, Taiwan , pages 427–433. IEEE, 2003

work page 2003

[2] [2]

Vision meets robotics: The kitti dataset

Andreas Geiger, Philip Lenz, Christoph Stiller, and Raq uel Urtasun. Vision meets robotics: The kitti dataset. The International Journal of Robotics Research, 32(11):1231–1237, 2013

work page 2013

[3] [3]

Highly scalable appearanc e- only slam-fab-map 2.0

Mark Cummins and Paul Newman. Highly scalable appearanc e- only slam-fab-map 2.0. In Robotics: Science and systems , volume 5, page 17. Seattle, USA, 2009

work page 2009

[4] [4]

Activ e markov localization for mobile robots

Dieter Fox, Wolfram Burgard, and Sebastian Thrun. Activ e markov localization for mobile robots. Robotics and Autonomous Systems , 25(3-4):195–207, 1998

work page 1998

[5] [5]

Exp loration with active loop-closing for fastslam

Cyrill Stachniss, Dirk Hahnel, and Wolfram Burgard. Exp loration with active loop-closing for fastslam. In 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(IEEE C at. No. 04CH37566), volume 2, pages 1505–1510. IEEE, 2004

work page 2004

[6] [6]

Active slam a nd loop prediction with the segmented map using simpliﬁed models

Nathaniel Fairﬁeld and David Wettergreen. Active slam a nd loop prediction with the segmented map using simpliﬁed models. I n Field and Service Robotics: Results of the 7th International Conf erence, pages 173–182. Springer, 2010

work page 2010

[7] [7]

Active slam using connectivity graph s as priors

Alberto Soragna, Marco Baldini, Dominik Joho, Rainer K¨ ummerle, and Giorgio Grisetti. Active slam using connectivity graph s as priors. In 2019 IEEE/RSJ International Conference on Intelligent Rob ots and Systems (IROS) , pages 340–346. IEEE, 2019

work page 2019

[8] [8]

A sur vey on active simultaneous localization and mapping: State of the art and new frontiers

Julio A Placed, Jared Strader, Henry Carrillo, Nikolay A tanasov, V adim Indelman, Luca Carlone, and Jos´ e A Castellanos. A sur vey on active simultaneous localization and mapping: State of the art and new frontiers. IEEE Transactions on Robotics , 39(3):1686–1705, 2023

work page 2023