Contextual Navigation Systems

VPS leverages the power of computer vision to provide highly accurate location data. Unlike GPS, which relies on satellite signals, VPS uses visual data from a device's camera to compare with a database of images, pinpointing the device's location in real-time. 𝐓𝐡𝐢𝐬 𝐦𝐞𝐭𝐡𝐨𝐝 𝐨𝐟𝐟𝐞𝐫𝐬 𝐬𝐞𝐯𝐞𝐫𝐚𝐥 𝐚𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬: >> Enhanced Accuracy: By utilizing visual landmarks, VPS can achieve greater accuracy, especially in urban environments where GPS signals may be weak or obstructed. >> Contextual Awareness: VPS provides not just location data but also contextual information about the surroundings, helping users understand their environment better. >> Indoor Navigation: Traditional GPS struggles indoors, but VPS can navigate complex indoor spaces like shopping malls, airports, and large office buildings. As our world becomes increasingly interconnected and reliant on precise location data, the limitations of current GPS technology become more apparent. 𝐕𝐏𝐒 𝐩𝐫𝐨𝐦𝐢𝐬𝐞𝐬 𝐭𝐨 𝐚𝐝𝐝𝐫𝐞𝐬𝐬 𝐭𝐡𝐞𝐬𝐞 𝐬𝐡𝐨𝐫𝐭𝐜𝐨𝐦𝐢𝐧𝐠𝐬, 𝐩𝐚𝐯𝐢𝐧𝐠 𝐭𝐡𝐞 𝐰𝐚𝐲 𝐟𝐨𝐫 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐦𝐞𝐧𝐭𝐬 𝐢𝐧 𝐯𝐚𝐫𝐢𝐨𝐮𝐬 𝐟𝐢𝐞𝐥𝐝𝐬: >> Urban Mobility: Improved navigation for drivers, cyclists, and pedestrians in densely populated areas. >> Augmented Reality (AR): Seamless integration of AR applications, enhancing user experiences with accurate, real-time location data. >> Retail and Logistics: Optimized indoor navigation for efficient movement of goods and people within large facilities. Question arises as to how often the pre-existing database of images should be updated. 🤔 How do you envision VPS transforming the way we navigate and interact with our environment? What potential applications of VPS are you most excited about? #innovation #technology #future #management #startups

Knowledge Graphs vs Context Graphs: Maps vs GPS The terms "context" and "graph" are causing confusion between the AI and Knowledge Graph crowds. So I've put together my thoughts on the difference. We are in early days but maybe we need another term other than context. A knowledge graph is a map. A carefully crafted, predefined, reference of what exists and how things relate. Cartography. You consult and query it. It answers: "Here's what we know." A context graph is your GPS navigation. It's not showing you the territory. It's pointing the way based on where you want to go (your current destination), recording where you are, where you've been, what turns you took, and what options you have right now. It's not a reference, it's a runtime trace. The map doesn't change when you drive. The GPS navigation does! Knowledge graphs are built. Context graphs are generated, every time an agent runs. Foundation Capital recently called context graphs "AI's trillion dollar opportunity", describing them as "a living record of decision traces stitched across entities and time." Systems of record for decisions, not just objects. That's the accumulated view, but those traces have to come from somewhere. Here's the lifecycle: Live: The agent navigates the context graph in real time (your current GPS route) Persisted: Execution completes, the context graph becomes a decision trace (your trip history) Accumulated: Decision traces become organisational memory (all trips, searchable precedent) Same structure, different lifecycle stage. They're describing the accumulated trail, the searchable history of why decisions were allowed to happen. I'm describing the moment of creation, the runtime execution topology the agent actually navigates. One becomes the other. The session-scoped execution graph produces the traces that become organisational memory. Here's where it gets interesting. Context graphs are rooted in process execution, not just data modelling. They're closer to a query plan, a state machine, or a runtime call graph than they are to an ontology. Dynamic state machines, not predefined procedures and workflows. This is where AI is forcing convergence between the data and software worlds. KGs asks "what exists and how does it relate?" Process execution asks "what's happening, what can happen next, and what triggered this?" Context graphs need both. Agents don't just query knowledge. They execute processes, discovering and navigating state transitions dynamically. The context graph is the structure they navigate. It's data and execution fused together. That's why this conversation is so valuable. The future belongs to teams that can blend knowledge representation with processing models. Neither discipline alone gets you there. - A knowledge graph tells you what exists. - A context graph tells you what happened, what's happening, and why, assembled for this specific journey. One is cartography. The other is navigation.

NaviRAG: What if your RAG system could browse knowledge the way you browse a library — not by guessing which shelf has the answer, but by walking the aisles, narrowing down, and pulling exactly what it needs? That's the core idea behind NaviRAG, a new paper from Tsinghua, Nanjing, and Northeastern University. 👉 WHY this matters Most RAG systems work like a keyword search on a messy desk. You chunk documents into fixed pieces, embed them, and hope the right piece floats to the top. This creates an impossible trade-off. Small chunks match well but lose surrounding context. Large chunks preserve context but drown the signal in noise. For simple factual lookups, this is fine. But when a question requires connecting clues scattered across a long document — each at a different level of detail — flat retrieval breaks down. Think of a detective question: "Who entered the study during the exact window the camera malfunctioned?" You first need to find when the camera failed (broad context), then zoom into entry records during that window (specific detail). A single similarity search can't handle both steps. 👉 WHAT NaviRAG does differently The authors reframe retrieval as navigation. Instead of one-shot search, the system actively explores a structured knowledge space in stages. Two key ideas: 1. Hierarchical Knowledge Organization — Documents are restructured offline into a tree. Top nodes hold broad summaries; deeper nodes hold specific details. Every node traces back to original text, so nothing is fabricated. 2. Two-Stage Navigational Retrieval — At query time, vector search first identifies relevant regions of the tree (narrowing the search space). Then an LLM agent walks top-down through those subtrees, deciding at each level: "Do I have enough here, or should I go deeper?" It collects evidence at whatever granularity the question demands. The result is a multi-granularity context set — broad summaries, mid-level descriptions, and raw passages — assembled dynamically per query. 👉 HOW well does it work Tested on NarrativeQA, LooGLE, and LongBench-v2 across multiple LLMs (Qwen3, LLaMA 3.3): - Retrieval recall improves by ~5% on NarrativeQA consistently across model sizes - Strongest gains on long-dependency tasks requiring cross-section evidence integration - Outperforms GraphRAG, LightRAG, and HippoRAG2 on complex reasoning while using far fewer context tokens than LightRAG (3,305 vs 18,896 tokens) - At top-k=3, NaviRAG matches what vanilla RAG needs top-k=15 to achieve Ablations confirm both components are essential — remove the hierarchy and navigation degenerates into local filtering; remove navigation and you lose progressive evidence localization. An interesting finding: NaviRAG shines on texts with strong semantic continuity (scripts, narratives) rather than modular documents (Wikipedia). The authors note this as an open direction.

Here is something very practical from VTT for your everyday life: 📍 Beyond maps: Adding context to navigation VTT has recently done the technical implementation of the Extended Trip Planner (XTP) concept. The basic idea of XTP is simple: 👉 traditional route planning is often not enough in complex or unfamiliar environments. What XTP adds: 🧭 Enriched navigation, not just a line on a map 📸 Location‑aware visual guidance (photos and other media) linked to: • specific route segments • exact geolocations 🗺️ A combined view: • conventional map‑based routing • contextual visual cues shown at the right time and place From a technical perspective, the demo shows: • How guidance content is attached to route segments, not just destinations • How this content is: ↳ retrieved via APIs ↳ dynamically presented during trip planning and navigation • How the solution builds on an existing Digitransit‑based route planner, instead of reinventing the basics • A new modular architecture that integrates enriched guidance as an extension, not a replacement The benefits: ✅ Better orientation ✅ Reduced uncertainty ✅ Support for environments where: • signage is poor • maps are ambiguous • cognitive load is high TLDR: Maps tell you where to go. Context helps you understand what to expect when you get there. This work has been done in the DeMO project, funded by Business Finland. The video below is obviously from VTT.

Most navigation problems are context problems, not screen problems. I implemented shared transitions to keep context between screens, especially around profiles, tasks, and rewards. Without transitions, navigation can feel abrupt. With shared elements, users keep a visual anchor while moving through flows. What I used: - shared element keys for profile/task/reward identity - shared bounds where resizing context matters - fallback behavior when shared scope is unavailable Why this mattered for StarJar: children understand movement and continuity faster than static screen swaps. That reduced confusion during navigation. Shared transitions are not just visual polish. They are navigation clarity. https://lnkd.in/g66U_MjP #BuildInPublic #ComposeMultiplatform #AndroidDev #UXDesign #MotionDesign #MobileEngineering #SoftwareCraft

AURA: Multimodal Shared Autonomy for Real-World Urban Navigation 🔁 At a Glance 💡 Goal: Develop a shared autonomy system that reduces human effort in urban navigation. ⚙️ Approach: Spatial-Aware Instruction Encoder (SIE): Ground human instructions in spatial context Multi-modal dataset MM-CoS: Collect diverse real-world data for training Diffusion policy: Generate safe trajectories based on environment and instructions 📈 Impact (Key Results) 🧪 Instruction following: Outperforms baselines with lower L2 error, especially with geometric instructions Reduces human operation effort by over 70% 🔄 Shared control: Decreases takeover frequency by more than 44% Increases robustness and efficiency in complex environments 🤖 Real-world deployment: Lowest intervention rates, with a Time Success Rate of 89.3% 🔬 Experiments 🧪 Benchmarks: MM-CoS, real sidewalk environments 🎯 Tasks: Long-horizon urban navigation 🦾 Setup: Urban robots, simulation and field tests 📐 Inputs: Visual and language instructions → Trajectory control 🛠 How to Implement 1️⃣ Gather diverse urban navigation data with VLMs 2️⃣ Train the Spatial-Aware Instruction Encoder 3️⃣ Develop the diffusion-based trajectory planner 4️⃣ Integrate instruction types (text, drafting, arrowing) 5️⃣ Deploy and validate in real-world scenarios 📦 Deployment Benefits ✅ Reduced operator workload ✅ Increased safety and robustness ✅ Long-term stable navigation ✅ No hardware modifications needed 📣 Takeaway This hierarchical, multimodal shared autonomy system improves urban navigation safety and efficiency. It effectively grounds human instructions in spatial context, enabling scalable, real-time robot assistance. Such advancements are crucial for deploying trustworthy autonomous systems in complex city environments. Follow me to know more about AI, ML and Robotics!