arxiv: 2604.12753 · v1 · submitted 2026-04-14 · 💻 cs.RO

Recognition: unknown

Reliability-Guided Depth Fusion for Glare-Resilient Navigation Costmaps

Shang-En Tsai (1) , Wei-Cheng Sun (1) ((1) Department of Computer Science , Information Engineering , Chang Jung Christian University)

Authors on Pith no claims yet

Pith reviewed 2026-05-10 14:57 UTC · model grok-4.3

classification 💻 cs.RO

keywords depth reliability mapglare resilient navigationRGB-D fusionoccupancy costmapsspecular interferencerobot navigationfree space preservationphantom obstacles

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The pith

A depth reliability estimator lets robots build costmaps that ignore glare from reflective floors and glass.

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

This paper seeks to prevent glare on shiny floors and glass from creating false obstacles in the maps robots use to navigate. It does so by training a model to judge how reliable each depth measurement is and then only trusting the good ones when updating the occupancy grid. Readers should care because indoor robots often fail or take bad paths when their maps fill with phantom barriers from bad sensor data. The method keeps the computation light enough for small onboard computers while showing better map accuracy in real tests.

Core claim

The authors claim that by explicitly estimating a per-pixel Depth Reliability Map for RGB-D measurements under specular interference and applying Reliability-Guided Fusion to occupancy updates, the system substantially reduces false obstacle insertion and improves free-space preservation in navigation costmaps, as validated on a real mobile platform with an Intel RealSense D435 and Jetson Orin Nano.

What carries the argument

The Depth Reliability Map estimator that predicts measurement trustworthiness per pixel, combined with the Reliability-Guided Fusion mechanism that modulates how updates enter the costmap.

Load-bearing premise

The Depth Reliability Map estimator can accurately predict per-pixel measurement trustworthiness under specular interference.

What would settle it

A direct comparison showing that the proposed fusion still produces as many phantom obstacles as standard methods in controlled tests with known glare sources would disprove the effectiveness of the reliability guidance.

Figures

Figures reproduced from arXiv: 2604.12753 by Chang Jung Christian University), Information Engineering, Shang-En Tsai (1), Wei-Cheng Sun (1) ((1) Department of Computer Science.

**Figure 1.** Figure 1: Overview of the proposed DRM+RGF framework for glare-resilient navigation costmaps. DRMNet predicts a per-pixel depth reliability map from RGB-D input, and reliability-guided fusion suppresses glare-induced phantom obstacles before generating the 2-D costmap for navigation. Contributions: 1. A glare-aware DRM estimator that quantifies per-pixel depth reliability in the presence of specular interference; 2… view at source ↗

**Figure 4.** Figure 4: Trajectory detour analysis under severe glare (L2). (a) Top-down view of the reflective corridor. (b) Baseline fusion produces a large detour due to glare-induced phantom obstacles. (c) DRM+RGF preserves a near-straight trajectory through the reflective region.Discussion Why reliability-guided fusion instead of full depth completion? Depth completion methods achieve strong reconstruction performance on tra… view at source ↗

read the original abstract

Specular glare on reflective floors and glass surfaces frequently corrupts RGB-D depth measurements, producing holes and spikes that accumulate as persistent phantom obstacles in occupancy-grid costmaps. This paper proposes a glare-resilient costmap construction method based on explicit depth-reliability modeling. A lightweight Depth Reliability Map (DRM) estimator predicts per-pixel measurement trustworthiness under specular interference, and a Reliability-Guided Fusion (RGF) mechanism uses this signal to modulate occupancy updates before corrupted measurements are accumulated into the map. Experiments on a real mobile robotic platform equipped with an Intel RealSense D435 and a Jetson Orin Nano show that the proposed method substantially reduces false obstacle insertion and improves free-space preservation under real reflective-floor and glass-surface conditions, while introducing only modest computational overhead. These results indicate that treating glare as a measurement-reliability problem provides a practical and lightweight solution for improving costmap correctness and navigation robustness in safety-critical indoor environments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper gives a practical, lightweight way to handle glare-induced depth errors in indoor robot costmaps but leaves the core DRM predictor without direct accuracy checks.

read the letter

The useful bit is the explicit modeling of per-pixel depth reliability for specular glare on floors and glass. They build a DRM estimator that scores trustworthiness, then feed it into RGF to modulate updates into the occupancy grid before bad readings turn into persistent phantom obstacles. This is a targeted application rather than a general fusion overhaul, and it runs light enough for a Jetson Orin Nano with a RealSense D435. Real-robot tests are claimed to cut false obstacles and keep more free space under reflective conditions, which matches a recurring deployment pain point.

Referee Report

2 major / 2 minor

Summary. The paper proposes a glare-resilient costmap construction method for RGB-D sensors that uses a lightweight Depth Reliability Map (DRM) estimator to predict per-pixel measurement trustworthiness under specular interference from reflective floors and glass, combined with a Reliability-Guided Fusion (RGF) mechanism to modulate occupancy updates and prevent accumulation of corrupted depth values as phantom obstacles. Real-robot experiments on a platform with an Intel RealSense D435 and Jetson Orin Nano are claimed to show substantial reductions in false obstacle insertion, improved free-space preservation, and only modest computational overhead.

Significance. If the central claim holds, this provides a practical, low-overhead approach to a persistent problem in indoor mobile robotics navigation, where specular reflections commonly degrade depth data and occupancy grids. Explicit reliability modeling could enhance safety-critical performance without requiring heavy sensor fusion or post-processing, and the real-robot validation (if quantified) would strengthen its applicability.

major comments (2)

[Experiments] Experiments section: the central claim of 'substantially reduces false obstacle insertion and improves free-space preservation' is presented without any quantitative metrics, baselines, error bars, statistical tests, or data exclusion criteria. This leaves the reported improvements unverifiable and prevents assessment of whether gains are attributable to DRM/RGF or generic filtering.
[Method] Method section (DRM estimator description): no standalone quantitative validation of the Depth Reliability Map is reported (e.g., pixel-wise precision, recall, or correlation against ground-truth reliability labels from multi-view consistency, controlled glare tests, or secondary sensors). Without this, the assumption that DRM correctly flags specular corruption remains untested, undermining attribution of costmap improvements to reliability guidance.

minor comments (2)

[Method] The abstract and method descriptions treat DRM and RGF as novel constructions but provide no pseudocode, parameter values, or implementation details that would aid reproducibility.
[Experiments] Figure captions and experimental setup descriptions could more explicitly state sensor parameters (e.g., depth range, exposure settings) and environmental conditions to contextualize the glare scenarios.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We agree that the current presentation of results would benefit from additional quantitative support and will revise the manuscript to strengthen the experimental and method sections accordingly.

read point-by-point responses

Referee: [Experiments] Experiments section: the central claim of 'substantially reduces false obstacle insertion and improves free-space preservation' is presented without any quantitative metrics, baselines, error bars, statistical tests, or data exclusion criteria. This leaves the reported improvements unverifiable and prevents assessment of whether gains are attributable to DRM/RGF or generic filtering.

Authors: We acknowledge that the experiments section currently emphasizes qualitative real-robot demonstrations without accompanying quantitative metrics. In the revised manuscript we will add explicit quantitative evaluations, including false-obstacle insertion rates, free-space preservation percentages, and direct comparisons against baselines (standard occupancy-grid mapping and simple depth filtering). Multiple independent trials will be reported with error bars and basic statistical tests to allow verification and clearer attribution of gains to the DRM/RGF components. revision: yes
Referee: [Method] Method section (DRM estimator description): no standalone quantitative validation of the Depth Reliability Map is reported (e.g., pixel-wise precision, recall, or correlation against ground-truth reliability labels from multi-view consistency, controlled glare tests, or secondary sensors). Without this, the assumption that DRM correctly flags specular corruption remains untested, undermining attribution of costmap improvements to reliability guidance.

Authors: We agree that a dedicated quantitative validation of the DRM estimator is currently absent and would strengthen the attribution of results. The revised manuscript will include a new evaluation subsection reporting pixel-wise precision, recall, and correlation metrics for the DRM. Ground-truth reliability labels will be derived from multi-view consistency checks performed under controlled glare conditions on reflective surfaces, directly testing the estimator's ability to identify specular corruption. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation or claims

full rationale

The paper introduces a new construction (DRM estimator + RGF fusion) for handling specular glare in RGB-D costmaps. No equations, parameter fits, or derivations are presented that reduce any prediction or result to its own inputs by construction. Central claims rest on external real-robot experiments rather than self-referential definitions, self-citation chains, or renamed known results. This is the normal case of a self-contained engineering method paper.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claim rests on the unverified effectiveness of two newly introduced components whose internal design, training data, and failure modes are not detailed in the abstract; no free parameters or external axioms are explicitly stated.

axioms (1)

domain assumption Depth measurements under specular glare can be usefully modeled by a per-pixel reliability score that is predictable by a lightweight estimator
Invoked by the proposal of the DRM estimator as the basis for modulating occupancy updates.

invented entities (2)

Depth Reliability Map (DRM) no independent evidence
purpose: Predicts per-pixel trustworthiness of depth measurements under specular interference
Newly introduced estimator whose accuracy is central to the fusion step but lacks independent evidence in the abstract.
Reliability-Guided Fusion (RGF) no independent evidence
purpose: Modulates occupancy-grid updates to prevent accumulation of corrupted depth data
New fusion mechanism whose behavior depends on the DRM output and is not shown to reduce to prior methods.

pith-pipeline@v0.9.0 · 5474 in / 1478 out tokens · 87956 ms · 2026-05-10T14:57:09.933554+00:00 · methodology

discussion (0)

Reference graph

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