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Posted on Originally published at v-modal.com

Android Robot SDK: Time Synchronization and Sensor Timestamp Management

#android #kotlin #robotics #ntp

Android Robot SDK: Time Synchronization and Sensor Timestamp Management

When multi-sensor arrays (LiDAR, IMU, Depth Cameras) feed point clouds into spatial mapping algorithms (SLAM), temporal alignment errors of even 10 milliseconds result in distorted maps or catastrophic localization failures. Achieving synchronized sensor fusion requires clock synchronization between Android devices and sub-systems using Precision Time Protocol (PTP IEEE 1588) or Network Time Protocol (NTP).

This tutorial details how to compute network clock offsets, estimate drift, and convert monotonic clock readings into synchronized master epochs.

1. Clock Synchronization Mathematical Model 

   Host (Android)                                      Robot Hardware (Master)
  T1 (Client Request Sent) --------------------------> T2 (Server Received)
                                                           |
  T4 (Client Reply Recv)  <-------------------------- T3 (Server Sent Reply)
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Offset Calculation:
$$ ext{Offset} = rac{(T2 - T1) + (T3 - T4)}{2}$$

Round Trip Delay (RTT):
$$ ext{RTT} = (T4 - T1) - (T3 - T2)$$

2. Implementing Clock Offset Estimation Engine 

package com.vmodal.sdk.timesync

import java.time.Instant

data class ClockOffsetSample(
    val offsetNanos: Long,
    val roundTripTimeNanos: Long,
    val timestamp: Instant = Instant.now()
)

class ClockSyncEngine {

    private val samples = mutableListOf<ClockOffsetSample>()
    private val maxSamples = 20

    /**
     * Compute NTP/PTP offset given 4 timestamp markers in nanoseconds.
     */
    fun recordTimestamps(t1Nanos: Long, t2Nanos: Long, t3Nanos: Long, t4Nanos: Long) {
        val rtt = (t4Nanos - t1Nanos) - (t3Nanos - t2Nanos)
        val offset = ((t2Nanos - t1Nanos) + (t3Nanos - t4Nanos)) / 2

        synchronized(samples) {
            if (samples.size >= maxSamples) {
                samples.removeAt(0)
            }
            samples.add(ClockOffsetSample(offsetNanos = offset, roundTripTimeNanos = rtt))
        }
    }

    /**
     * Filter out high-jitter outliers and calculate weighted average offset.
     */
    fun getEstimatedOffsetNanos(): Long {
        synchronized(samples) {
            if (samples.isEmpty()) return 0L
            // Sort by RTT and take the best 50% lowest jitter samples
            val sorted = samples.sortedBy { it.roundTripTimeNanos }
            val bestSamples = sorted.take((sorted.size / 2).coerceAtLeast(1))
            return bestSamples.map { it.offsetNanos }.average().toLong()
        }
    }
}
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3. High-Precision Sensor Timestamp Converter

Android system clocks (SystemClock.elapsedRealtimeNanos()) must be converted seamlessly into synchronized master clock timestamps.

package com.vmodal.sdk.timesync

import android.os.SystemClock

class HighPrecisionTimestampConverter(
    private val syncEngine: ClockSyncEngine
) {
    /**
     * Convert local android monotonic timestamp to synchronized Robot Master Epoch (nanoseconds)
     */
    fun toSynchronizedMasterEpochNanos(localMonotonicNanos: Long): Long {
        val estimatedOffset = syncEngine.getEstimatedOffsetNanos()
        return localMonotonicNanos + estimatedOffset
    }
}
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4. End-to-End Example 

import com.vmodal.sdk.timesync.ClockSyncEngine
import com.vmodal.sdk.timesync.HighPrecisionTimestampConverter

fun main() {
    val syncEngine = ClockSyncEngine()

    // Simulate clock handshake timestamps
    val t1 = 1_000_000_000L
    val t2 = 1_000_050_000L
    val t3 = 1_000_051_000L
    val t4 = 1_000_105_000L

    syncEngine.recordTimestamps(t1, t2, t3, t4)

    val converter = HighPrecisionTimestampConverter(syncEngine)
    val localSensorTime = 2_500_000_000L
    val syncedMasterTime = converter.toSynchronizedMasterEpochNanos(localSensorTime)

    println("Estimated Clock Offset: ${syncEngine.getEstimatedOffsetNanos()} ns")
    println("Original Local Sensor Time: $localSensorTime ns")
    println("Synchronized Master Epoch:  $syncedMasterTime ns")
}
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Conclusion

Precise clock offset filtering and low-jitter timestamp conversions ensure your multi-sensor streams remain aligned across multi-node robotic hardware systems.

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