The Role of Motion-and-Visual Perception in Robot Place Learning and Navigation

Marvin Chancán

Ph.D. Thesis


Autonomous robot navigation primarily relies on motion and visual perception (MVP) with feedback control from the environment. While vision-based robot navigation algorithms have largely been constrained to indoor, small outdoor, or simulation settings, their generalization under extreme appearance changes has not yet been extensively investigated. Conversely, traditional motion-based robot place recognition pipelines perform surprisingly well under severe variations in weather, climate or illumination; yet they exhibit significant disadvantages for navigation applications such as high latency and storage requirements. In this thesis, I study the role of joint MVP-based end-to-end learning in both place recognition and navigation within modern reinforcement learning and deep learning frameworks. For robot place recognition, I propose two types of high-performance neural architectures, FlyNet+CANN and DeepSeqSLAM, along with novel sequence processing methods for learning motion-driven representations from MVP data. Similar to classical two-stage heuristic-based pipelines, FlyNet+CANN requires both training and testing data for deployment but performs sequential place recognition using an entire neural implementation; yet its CANN component requires careful fine-tuning prior to deployment. In contrast, DeepSeqSLAM can be trained end-to-end to efficiently perform sequential place learning - an idea formally introduced here - from a single training traversal of an environment, while robustly generalizing under severe changing conditions; although its accuracy relies on precise motion estimation. For robot navigation, I introduce the interactive CityLearn simulation environment for all-weather, city-scale training and testing of navigation agents, based on simple move forward/backward commands, along with new sample-efficient neural architectures. CityLearn features over ten driving datasets from 60+ cities around the world, while allowing the use of any other dataset such as those from drones or underwater robots. Together, the proposed methods set new state-of-the-art performance standards with higher throughput and lower latency, representing a significant step towards the deployment of new robot learning-based SLAM and autonomous navigation systems in the real world.

Thesis: [PDF]   [ePrint]

PhD Thesis Defense


          author = {Marvin Aldo Chancan Leon},
           title = {The role of motion-and-visual perception in robot place learning and navigation},
          school = {Queensland University of Technology},
            year = {2022},
             doi = {10.5204/thesis.eprints.229769},
        keywords = {mobile robots, navigation, place recognition, motion estimation, deep learning, reinforcement learning, artificial neural networks, convolutional neural networks, recurrent neural networks, continuous attractor neural networks},
             url = {}

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