Venue: CoRL 2021 Conference

Reviews:

final_score: rating: Weak Accept: I recommend accepting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary: The authors present a self-improving perception framework in the context of robot localization against 3D models/floor plans of changing environments. The localization is aided by a DNN, which segments the current scene into foreground (novel objects, not used for scan matching) and background (consistent with the 3D model, used for scan matching) points. The focus of this work is on how to train this network in a self-improving manner while adapting to new environments. To this end, authors propose a way to generate noisy pseudo labels from the current robot pose estimate, the current scan and the available 3D model. These pseudo labels are then used to train the DNN and thus improve its performance in the new environment. In turn, the better segmentation improves the localization itself. To avoid forgetting of already acquired knowledge during the deployment in other environments, authors use continual learning techniques such as memory replay. The experiments on a real robotic platform demonstrate that the approach is able to improve both localization and segmentation in novel environments, transfer acquired knowledge across environments and avoid catastrophic forgetting. 

strengths_and_weaknesses: Robots are deployed in a large variety of environments and the ability to adapt to new domains is a sought-after skill. Therefore, the paper contributes to a very relevant topic for the robotics community. It is well written and contains comprehensive experiments with valuable insights. I especially appreciate that the experiments were conducted on a real-world robotic platform. 

While the self-improving and continual learning framework is interesting, I find its demonstration/application scenario to be less appealing. Potentially, it is because the importance of the removal of the foreground points for the task at hand is not stressed enough in the introduction (unfortunately, the related citation is anonymized). I think one or two sentences elaborating on this matter could be beneficial for the reader. The chosen localization baseline with point-to-plane ICP seems to be quite basic and I was left wondering if with more robust scan-matching (Beyond points: Evaluating recent 3D scan-matching algorithms, Magnusson et al., ICRA'15) or a SLAM method (with e.g. lidar odometry which could even leverage the existence of new objects) would perform better at the localization task and in turn in foreground/background segmentation (with the same approach used for the generation of pseudo labels). This is neither discussed in the text nor investigated in the experiments. Also, if the authors could suggest more applications of the learnt binary segmentation, it could further highlight the importance of this work.
summary_of_recommendation: I simply think that the topic addressed in this work is very relevant to the robotics community and this work provides some valuable contributions.
issues: * For a less informed reader (like myself) on continual learning, could you please expand on the significance of the finding that CL works with noisy pseudo labels? Given the nature of memory replay and that noisy pseudo labels have been successfully used in self-training settings, I don’t find this as a surprising or very insightful outcome. This is a genuine question, without trying to reduce your contribution.
* For the localization, do you use robot’s odometry? 
* How would your approach scale in terms of accuracy, forgetting and computational efficiency when the robot is deployed in more than two environments e.g. garage -> construction -> office, etc.?
* How does your work compare to *Self-Supervised Learning of Lidar Segmentation
for Autonomous Indoor Navigation, Hugues et al., ICRA'21*? I understand that it is a very recent and parallel work, but I think it should be discussed in the related work.
* The online learning experiment (Sec. 4.3) seems to be inconclusive and the description lacks details e.g. in which environment the experiment is performed? Is the network initially pre-trained on the NYU dataset? Do you observe similar outcomes in the rest of the environments?
* In general, for most of the experiments one could think of applying several iterations (like it is usual in self-training) i.e. improved segmentation -> improved localization -> improved pseudo labels -> improved segmentation -> ... Have you tried that?
review_title: null

final_score: rating: Weak Accept: I recommend accepting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary: This paper proposed a self-supervised continual learning strategy on scene perception where the perception task is a background/foreground binary classification. Self-supervised learning is based on generated pseudo-labels on localised LiDAR scan, where each point are labelled based on how close to the nearest floorplan via a defined threshold. By further applying a memory buffer storing on 10% of previous scene data, it achieves continual learning and shows that training on new scenes could help learn a better representation improving performances of the previous scenes.
strengths_and_weaknesses: The general concept of this paper is appealing, and I appreciate the video attached shows how this robot with the proposed method performs in a real-world environment. 

Some additional comments:

-- Limited novelty: The proposed method is based on components that are standard and commonly used in the community, for example, the random buffer is typically used in perception-based continual-learning systems, and in most RL-based methods. Though I agree the design of pseudo-labeling is new and interesting, but limited to the task of only performing binary segmentation is a bit too easy. The background class of the indoor room has a very clear geometric structure and thus transferring across different tasks would be simple and straightforward. Considering background and foreground segmentation as "perception" seems to be an over-claim to me.

-- Quality of visualisation: In the video, It's very hard for me to identify the ground-truth tracking and predicted tracking. 

-- Compared to other CL baselines: To the best of my knowledge (but happy to be corrected if wrong), EWC and other presented regularisation-based CL methods are designed with the assumption that we don't have access to any of the data in the previous tasks. While in the replay-based methods, the data are stored in the memory buffer and thus would be still trained when deploying in the new scenes. So it's a bit unfair to directly compare to these regularisation methods without properly justifying the differences.

=======================================================

The authors have clarified all my questions in the rebuttal phase. And I decide to raise my final rating to Weak Accept.
summary_of_recommendation: I like the overall system to be shown and work in a real-world environment. However, the paper is a bit overclaim on the impact and the novelty of the method.
issues: See weaknesses.
review_title: null

final_score: rating: Weak Reject: I recommend rejecting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary: This paper presents a self-supervised and continual learning framework for improving indoor foreground-background segmentation and robot localization with known maps/models. The self-supervised learning approach involves generating pseudo segmentation labels from LiDAR scans and using them to fine-tune a segmentation model pre-trained with an offline segmentation dataset (NYU-Depth). The paper also extends this to continual learning where the robot acquires pseudo-labels on the fly and fine-tunes the model in new environments. This involves using a memory replay buffer to avoid catastrophic forgetting of previous experiences. Experimental results show that both the self-supervised and continual learning lead to significant error reductions in foreground-background segmentation.
strengths_and_weaknesses: Strengths:
- In general, self-supervised and continual learning are both quite relevant for real-world robotic systems. While data-driven paradigms have dominated vision methods, in robotics, systems have to rapidly adapt to new environments. And learning could be an interesting solution here.
- The paper presents a real-robot setup with real-world evaluations. 

Weaknesses:
- The self-supervised learning approach uses pseudo-labels generated by thresholding LiDAR scans to mask out foreground from background, but there is no baseline that investigates how good these pseudo-labels themselves are. What if you don't learn anything (self-supervised or continual) and directly use these pseudo-labels for segmentation? How well does this perform? The pseudo-labels might be noisy, but this depends on what sort of accuracy is necessary for the end-task, which is undefined. I am not sure if this pseudo-label result is already in Table 1; I had a hard time connecting the descriptions in Section 4.1 to items in Table 1.
- The results emphasize 60% gains in foreground-background segmentation from the proposed approach, but the paper does not mention a strong use-case for why foreground-background is necessary or important for a specific task. The current setup assumes access to LiDAR+vision and maps/models of the environment, so I am not sure what foreground-background segmentation offers in this scenario. Obstacle avoidance and localization can be done with LiDAR, without vision. Further, the segmentation labels do not classify object categories, so they cannot be used for task-planning. 
- While the real-world evaluations are really great, they are hard to reproduce. Would it be possible to benchmark the performance gains in a simulated setup? Compared to the previous concerns, this is relatively minor, but nonetheless important. It’s hard to generate photorealistic simulations of construction and parking sites, but equivalent scenes from NVIDIA Isaac Sim or just setting-up a Gazebo environment might be feasible. While the 10% improvements in localization is great, this result can be substantially strengthened if it can be replicated in a reproducible benchmark. Similarly, the experiments in Section 4.3 need to be empirically more rigorous. What happens after 250 secs? Why does the plot stop exactly when the segmentation quality peaks?

summary_of_recommendation: While the proposed self-supervised and continual learning approaches are interesting, the evaluation is missing an important baseline and a strong motivating use-case for foreground-background segmentation.

**Post Rebuttal**  
I thank the authors for the detailed follow-up responses. Without the pseudo-label baseline, I find it hard to appreciate the value of the proposed learning-based methods. "we never claim that different solutions to the application would not be possible" -> I understand that there is merit to working on approaches that have other viable alternatives, but without empirical evaluations against simpler baselines, it's hard for the community to figure out what is the best way to _solve the problem_ (i.e. localization), even if the novel approach might be promising for some future applications. So I am keeping my original score of Weak Reject.
issues: Major
- See comments above on evaluation.

Minor
- L60: These contributions can be summarized succinctly. For example, the pseudo-label generation can be part of the self-improving framework.
review_title: null

final_score: rating: Strong Accept: I recommend accepting the paper and will argue for my recommendation even if other reviewers hold a different opinion.
review_content: summary: The paper combines self-supervision with methods from continual learning to enable a robot to improve its localization during deployment. More concretely, the proposed method automatically labels images that are collected during deployment based on localization information and uses them to continuously update a segmentation module, which in turn is used for localization. The authors show in a suite of real-world environments that this feedback loop between segmentation and localization modules significantly improves both the localization capabilities and the quality of generated segmentations. They also note that updating the segmentation module in an online fashion comes with the risks of catastrophic forgetting of previously learned features, and show that a simple memory buffer can prevent this from happening.
strengths_and_weaknesses: ### Strengths

- The main idea of enabling continual learning for robot localization via self-supervised segmentation labels is both interesting and well-executed.
- The paper is presented in a clear and concise fashion.
- The approach is applied to different real-world environments, showcasing its versatility and making for a convincing experimental section.
- A series of ablation experiments is conducted to show the influence of different hyperparameters and design decisions on the overall framework. Different methods of continual learning are explored and compared to the memory buffer chosen for the final model.

### Weaknesses

- The automatic segmentation labeling requires a ground truth floor-plan that is compared to LiDAR scans. This limits the applicability of the approach to indoor environments with precisely known layouts.
- While the use of a sample buffer helps prevent catastrophic forgetting, the ablations show that it comes with the cost of reduced performance on new environments. As such, the question of how many old samples to use becomes a hyperparameter that needs to be carefully tuned for a downstream application. Note however that this is well-addressed in the ablation studies.
- As stated in the "Limitations" section, some parameters need to be tuned depending on the environment. This raises the question of how well the method generalizes to different and potentially more complex environments.
summary_of_recommendation: The paper explores the idea of improving semantic segmentation during robot deployment by means of continual learning and self supervision. This is an interesting contribution on its own, and naturally opens up various directions for future work.

Presentation-wise, the main ideas of the paper are thoroughly motivated and explained clearly. The experimental section clearly shows the benefit over the approach over a baseline that does not use it, and a number of ablations give insight into the importance of individual components. The main shortcoming of the experiments seems to be the need for manual parameter tuning. Adding some measure of confidence (e.g., standard deviations) would help to make a better case for the approach.

Overall, the paper introduces and experimentally supports an interesting novel approach, and presents everything in a clearly understandable manner. I would thus recommend to accept it.


**Update**
The authors have addressed most of my concerns during the revision period. I am still not entirely satisfied with the scope and presentation of Section 4.3 (as well as the related Appendix 7), and agree with the other reviewers that the paper would benefit greatly from a pseudo-label baseline. However, the pros of the paper far outweigh the few problems I have with it, causing me to keep my score of "Strong Accept".
issues: - Figure 1 does not really seem to add much to the paper. I would much rather see a qualitative comparison, e.g., in the form of a suitable frame from the summary video in the supplement.
- The images in Figure 4 have pretty high resolution, which can lead to some lag when loading the page in a .pdf reader. Down-sampling them would help, and also decrease the overall size of the file.
- In the "Robotic Platform" subsection of Section 4, it says that images are captured at 20Hz and then matched to the 5Hz LiDAR scans, with offsets being corrected by hand. Is there a reason for the images to be captured at 20Hz instead of using 5Hz as the common denominator?
- The section on online learning (Section 4.3) is rather short. While the paper states that further investigation on this topic is needed, it would still be nice to present the existing results more clearly. For example, Figure 5 Row 3 shows that the localization results get worse for some time when online learning, before eventually improving upon the baseline. It would be interesting so see why this is the case. Similarly, Figure 5 Row 1 shows relative changes to a baseline. Adding some general trend, e.g., an exponential moving average or cumulative histogram of the changes, could help readability. Finally, it would be nice to see how statistically significant these changes are, though this may be out of scope for the revision.

### Misc.

- Line 43 "... has not been discovered before" seems like a rather strong statement. Adding some variant of "to the best of our knowledge" would alleviate this. A similar argument can be made in Line 277.
- In Table 3, the "61.5" in the last row is written in bold, which seems to be unintentional.
review_title: null

Meta-review: The paper proposes a continual learning perception framework for indoor robot localization on floor plans. All the reviewers find the problem being addressed in this work of importance and a required skill for robots. The paper is well written and clearly organized with real-world robotic experiments that are appreciated by the reviewers. Most of the concerns raised by the reviewers have been thoroughly addressed in the rebuttal. I thank the authors for the engaging discussions during the rebuttal. Some minor concerns still exist, primarily including a  pseudo-label baseline. Nevertheless, I agree with the reviewers that the paper is definitely an interesting contribution.
Decision: Accept (Poster)