Reliable robot perception requires not only predicting scene structure, but also identifying where predictions should be treated as unreliable due to sparse or missing observations. We present ContraMap, a contrastive continuous mapping method that augments kernel-based discriminative maps with an explicit uncertainty class trained using synthetic noise samples. This formulation treats unobserved regions as a contrastive class, enabling joint environment prediction and spatial uncertainty estimation in real time without Bayesian inference. Under a simple mixture-model view, we show that the probability assigned to the uncertainty class is a monotonic function of a distance-aware uncertainty surrogate. Experiments in 2D occupancy mapping, 3D semantic mapping, and tabletop scene reconstruction show that ContraMap preserves mapping quality, produces spatially coherent uncertainty estimates, and is substantially more efficient than Bayesian kernelmap baselines.
Figures & tables
Fig. 1 : For robust robot operation, scene representations should provide not only spatially consistent mapping but also a measure of uncertainty across the environment. ContraMap augments continuous classification-based mapping with an additional uncertainty output, enabling joint environment representation and direct uncertainty prediction at any queried location. The model reconstructs scene structure while assigning high uncertainty to occluded or weakly observed regions, such as the space behind the table.
Fig. 2 : Predictive uncertainty for a Gaussian Process (GP) and neural-network baselines on three toy datasets. Orange / Magenta points are in-distribution training samples, and Red points are out-of-distribution samples. Background shading indicates relative uncertainty (brighter = higher, darker = lower). Our method best matches the GP “gold standard”: it stays confident near observed data and becomes uncertain as inputs move away from the training distribution.
Fig. 3 : Overview of ContraMap . Our method employs a softmax classifier network with an additional output node to jointly represent the environment and estimate uncertainty. Observed data (e.g., LiDAR scan or segmented point cloud) are augmented with negative samples and noise, where the noise is labeled as an additional (C+1) -th or ”uncertain” class. The augmented data are then projected into a feature vector space using a set of reference points, forming the training dataset Dtrain . The model is trained on this dataset using a first-order optimization method. Once trained, we can query the model at arbitrary locations in the environment to obtain either environment mapping results or uncertainty estimates via the additional ”uncertain” node.
Datasets
Hilbert Maps [ 3 ]
Bayesian Hilbert Maps [ 8 ]
Our
Belgioioso (#features = 8400)
AUC ↑
0.9904 ( ± 0.0003)
0.9901 ( ± 0.0003)
0.9901 ( ± 0.0003)
Training time (s) ↓
2.2609 ( ± 0.0279)
722.6140 ( ± 2.5651)
3.2367 ( ± 0.0231)
Inference time (s) ↓
0.4258 ( ± 0.0014)
231.3024 ( ± 0.0746)
0.4681 ( ± 0.0022)
Edmonton (#features = 8800)
AUC ↑
0.9754 ( ± 0.0005)
0.9734 ( ± 0.0005)
0.9767 ( ± 0.0004)
Training time (s) ↓
1.8345 ( ± 0.0371)
789.5725 ( ± 1.7593)
2.2733 ( ± 0.0245)
Inference time (s) ↓
0.6997 ( ± 0.0016)
405.7368 ( ± 0.0842)
0.7630 ( ± 0.0011)
TABLE I : The performance of our method in comparison to Hilbert Map [ 3 ] and Bayesian Hilbert Map [ 8 ] on the Radish datasets [ 24 ] .
Metric
HMs
Our
mIoU ↑
0.8096 ± 0.0515
0.8412 ± 0.0394
Training Time (s) ↓
32.6278 ± 1.5994
35.9025 ± 1.7746
Inference Time (s) ↓
203.6160 ± 8.2965
203.7221 ± 8.3205
TABLE II : Quantitative comparison against 3D Hilbert Maps (HMs) on the SemanticKITTI dataset. Experiments were conducted on the initial scan of 10 distinct sequences. Bold indicates the statistically better result ( p<0.05 ).
Metric
HMs
Our
mIoU ↑
0.6622 ± 0.1234
0.6694 ± 0.1231
Training Time (s) ↓
32.0929 ± 22.6771
35.9777 ± 25.5202
Inference Time (s) ↓
32.2525 ± 2.6430
32.2989 ± 2.6393
TABLE III : Quantitative comparison against 3D Hilbert Maps (HMs) on the SceneNet dataset. The evaluation was performed across 60 distinct indoor scenes (rooms). Bold indicates the statistically better result ( p<0.05 ).
Fig. 4 : Occupancy mapping results of each method on Intel dataset.
Fig. 5 : Results extracted from each output of the softmax model.
Fig. 6 : Reconstruction results on SceneNet dataset.
Fig. 7 : Reconstruction results on SemanticKITTI dataset.
V-PRISM [ 10 ]
Our
IoU ↑
0.4994 ( ± 0.0878)
0.4854 ( ± 0.0805)
Chamfer (m) ↓
0.0126 ( ± 0.026)
0.0124 ( ± 0.031)
Training time (s) ↓
11.2826 ( ± 5.6768)
9.3533 ( ± 2.1653)
Reconstructing time (s) ↓
229.5563 ( ± 100.0576)
3.7560 ( ± 1.1183)
TABLE IV : The performance of our proposed method compared to V-PRISM [ 10 ] on YCB dataset.
Fig. 8 : Representative scene reconstructions. Col. 1: RGB. Col. 2: segmented point cloud. Col. 3: our reconstructed mesh. Col. 4: our uncertainty (softmax auxiliary head; lighter = higher). Col. 5: V-PRISM mesh. Col. 6: V-PRISM uncertainty heatmap (lighter = higher). Following [ 10 ] , Cols. 4 and 6 show uncertainty on a 2D scene slice (bottom = nearer the camera). Object boundaries from the top-view projection are overlaid for spatial context.
Fig. 9 : Effect of hinge point count on performance (Intel dataset). Results are shown for our methods with varying numbers of hidden layers and for BHMs .
In this paper, we describe a representation for spatial information, called the stochastic map, and associated procedures for building it, reading information from it, and revising it incrementally as new information is obtained. The map contains the estimates of relationships among objects in the map, and their uncertainties, given all the available information. The procedures provide a general solution to the problem of estimating uncertain relative spatial relationships. The estimates are probabilistic in nature, an advance over the previous, very conservative, worst-case approaches to the problem. Finally, the procedures are developed in the context of state-estimation and filtering theory, which provides a solid basis for numerous extensions.
Randall Smith, Matthew Self, Peter Cheeseman
SRI International 333 Ravenswood Avenue Menlo Park, California 94025 · General Motors Research Laboratories Warren, Michigan · UC Berkeley +1
Visual navigation in unknown environments remains a core challenge in mobile robotics, especially for resource-constrained platforms. Most existing approaches rely on loosely coupled modular pipelines and strong assumptions on perception quality or environmental structure, often resorting to multi-modal sensor suites that increase system complexity and deployment cost. Vision-only navigation offers a lightweight alternative, but its performance degrades severely under motion blur, low texture, and illumination changes, largely because they neglect the tight coupling between commanded motion and perception. While perception-aware methods partially address this issue, they typically optimize individual modules and fail to propagate uncertainty consistently across the navigation stack. In this paper, we present UNSEEN, a unified uncertainty- and perception-aware navigation framework that explicitly couples localization, mapping, and planning using only a front-mounted camera. UNSEEN estimates sparse maps and robot poses with associated uncertainties at 6Hz, and leverages them to plan trajectories that jointly optimize task progress and estimation accuracy in receding-horizon. Simulations and extensive real-world experiments in unknown environments demonstrate the robustness of the proposed approach, with UNSEEN-SLAM reducing absolute translational error by 9.8% and UNSEEN-Plan improving estimation accuracy by up to 45% compared to state-of-the-art methods, while achieving a 100% task success rate.
Tommaso Faraci, Marco Camurri, Daniele Fontanelli +1
This paper presents RichMap, a high-precision reachability map representation designed to balance efficiency and flexibility for versatile robot manipulation tasks. By refining the classic grid-based structure, we propose a streamlined approach that achieves performance close to compact map forms (e.g., RM4D) while maintaining structural flexibility. Our method utilizes theoretical capacity bounds on S2 (or SO(3)) to ensure rigorous coverage and employs an asynchronous pipeline for efficient construction. We validate the map against comprehensive metrics, pursuing high prediction accuracy (>98%), low false positive rates (1∼2%), and fast large-batch query (∼15 μs/query). We extend the framework applications to quantify robot workspace similarity via maximum mean discrepancy (MMD) metrics and demonstrate energy-based guidance for diffusion policy transfer, achieving up to 26% improvement for cross-embodiment scenarios in the block pushing experiment.
Yupu Lu, Yuxiang Ma, Jia Pan
School of Computing and Data Science, The University of Hong Kong, HKSAR · Massachusetts Institute of Technology, Cambridge, USA